Parallel Implementation of the Discontinuous Galerkin Method

NASA Technical Reports Server (NTRS)

Baggag, Abdalkader; Atkins, Harold; Keyes, David

1999-01-01

This paper describes a parallel implementation of the discontinuous Galerkin method. Discontinuous Galerkin is a spatially compact method that retains its accuracy and robustness on non-smooth unstructured grids and is well suited for time dependent simulations. Several parallelization approaches are studied and evaluated. The most natural and symmetric of the approaches has been implemented in all object-oriented code used to simulate aeroacoustic scattering. The parallel implementation is MPI-based and has been tested on various parallel platforms such as the SGI Origin, IBM SP2, and clusters of SGI and Sun workstations. The scalability results presented for the SGI Origin show slightly superlinear speedup on a fixed-size problem due to cache effects.

Large-scale three-dimensional phase-field simulations for phase coarsening at ultrahigh volume fraction on high-performance architectures

NASA Astrophysics Data System (ADS)

Yan, Hui; Wang, K. G.; Jones, Jim E.

2016-06-01

A parallel algorithm for large-scale three-dimensional phase-field simulations of phase coarsening is developed and implemented on high-performance architectures. From the large-scale simulations, a new kinetics in phase coarsening in the region of ultrahigh volume fraction is found. The parallel implementation is capable of harnessing the greater computer power available from high-performance architectures. The parallelized code enables increase in three-dimensional simulation system size up to a 5123 grid cube. Through the parallelized code, practical runtime can be achieved for three-dimensional large-scale simulations, and the statistical significance of the results from these high resolution parallel simulations are greatly improved over those obtainable from serial simulations. A detailed performance analysis on speed-up and scalability is presented, showing good scalability which improves with increasing problem size. In addition, a model for prediction of runtime is developed, which shows a good agreement with actual run time from numerical tests.

The Scalable Checkpoint/Restart Library

DOE Office of Scientific and Technical Information (OSTI.GOV)

Moody, A.

The Scalable Checkpoint/Restart (SCR) library provides an interface that codes may use to worite our and read in application-level checkpoints in a scalable fashion. In the current implementation, checkpoint files are cached in local storage (hard disk or RAM disk) on the compute nodes. This technique provides scalable aggregate bandwidth and uses storage resources that are fully dedicated to the job. This approach addresses the two common drawbacks of checkpointing a large-scale application to a shared parallel file system, namely, limited bandwidth and file system contention. In fact, on current platforms, SCR scales linearly with the number of compute nodes.more » It has been benchmarked as high as 720GB/s on 1094 nodes of Atlas, which is nearly two orders of magnitude faster thanthe parallel file system.« less

Implementation of the NAS Parallel Benchmarks in Java

NASA Technical Reports Server (NTRS)

Frumkin, Michael A.; Schultz, Matthew; Jin, Haoqiang; Yan, Jerry; Biegel, Bryan (Technical Monitor)

2002-01-01

Several features make Java an attractive choice for High Performance Computing (HPC). In order to gauge the applicability of Java to Computational Fluid Dynamics (CFD), we have implemented the NAS (NASA Advanced Supercomputing) Parallel Benchmarks in Java. The performance and scalability of the benchmarks point out the areas where improvement in Java compiler technology and in Java thread implementation would position Java closer to Fortran in the competition for CFD applications.

Parallel heuristics for scalable community detection

DOE PAGES

Lu, Hao; Halappanavar, Mahantesh; Kalyanaraman, Ananth

2015-08-14

Community detection has become a fundamental operation in numerous graph-theoretic applications. Despite its potential for application, there is only limited support for community detection on large-scale parallel computers, largely owing to the irregular and inherently sequential nature of the underlying heuristics. In this paper, we present parallelization heuristics for fast community detection using the Louvain method as the serial template. The Louvain method is an iterative heuristic for modularity optimization. Originally developed in 2008, the method has become increasingly popular owing to its ability to detect high modularity community partitions in a fast and memory-efficient manner. However, the method ismore » also inherently sequential, thereby limiting its scalability. Here, we observe certain key properties of this method that present challenges for its parallelization, and consequently propose heuristics that are designed to break the sequential barrier. For evaluation purposes, we implemented our heuristics using OpenMP multithreading, and tested them over real world graphs derived from multiple application domains. Compared to the serial Louvain implementation, our parallel implementation is able to produce community outputs with a higher modularity for most of the inputs tested, in comparable number or fewer iterations, while providing real speedups of up to 16x using 32 threads.« less

Implementation of NAS Parallel Benchmarks in Java

NASA Technical Reports Server (NTRS)

Frumkin, Michael; Schultz, Matthew; Jin, Hao-Qiang; Yan, Jerry

2000-01-01

A number of features make Java an attractive but a debatable choice for High Performance Computing (HPC). In order to gauge the applicability of Java to the Computational Fluid Dynamics (CFD) we have implemented NAS Parallel Benchmarks in Java. The performance and scalability of the benchmarks point out the areas where improvement in Java compiler technology and in Java thread implementation would move Java closer to Fortran in the competition for CFD applications.

Scalable and balanced dynamic hybrid data assimilation

NASA Astrophysics Data System (ADS)

Kauranne, Tuomo; Amour, Idrissa; Gunia, Martin; Kallio, Kari; Lepistö, Ahti; Koponen, Sampsa

2017-04-01

Scalability of complex weather forecasting suites is dependent on the technical tools available for implementing highly parallel computational kernels, but to an equally large extent also on the dependence patterns between various components of the suite, such as observation processing, data assimilation and the forecast model. Scalability is a particular challenge for 4D variational assimilation methods that necessarily couple the forecast model into the assimilation process and subject this combination to an inherently serial quasi-Newton minimization process. Ensemble based assimilation methods are naturally more parallel, but large models force ensemble sizes to be small and that results in poor assimilation accuracy, somewhat akin to shooting with a shotgun in a million-dimensional space. The Variational Ensemble Kalman Filter (VEnKF) is an ensemble method that can attain the accuracy of 4D variational data assimilation with a small ensemble size. It achieves this by processing a Gaussian approximation of the current error covariance distribution, instead of a set of ensemble members, analogously to the Extended Kalman Filter EKF. Ensemble members are re-sampled every time a new set of observations is processed from a new approximation of that Gaussian distribution which makes VEnKF a dynamic assimilation method. After this a smoothing step is applied that turns VEnKF into a dynamic Variational Ensemble Kalman Smoother VEnKS. In this smoothing step, the same process is iterated with frequent re-sampling of the ensemble but now using past iterations as surrogate observations until the end result is a smooth and balanced model trajectory. In principle, VEnKF could suffer from similar scalability issues as 4D-Var. However, this can be avoided by isolating the forecast model completely from the minimization process by implementing the latter as a wrapper code whose only link to the model is calling for many parallel and totally independent model runs, all of them implemented as parallel model runs themselves. The only bottleneck in the process is the gathering and scattering of initial and final model state snapshots before and after the parallel runs which requires a very efficient and low-latency communication network. However, the volume of data communicated is small and the intervening minimization steps are only 3D-Var, which means their computational load is negligible compared with the fully parallel model runs. We present example results of scalable VEnKF with the 4D lake and shallow sea model COHERENS, assimilating simultaneously continuous in situ measurements in a single point and infrequent satellite images that cover a whole lake, with the fully scalable VEnKF.

Parallel processing architecture for H.264 deblocking filter on multi-core platforms

NASA Astrophysics Data System (ADS)

Prasad, Durga P.; Sonachalam, Sekar; Kunchamwar, Mangesh K.; Gunupudi, Nageswara Rao

2012-03-01

Massively parallel computing (multi-core) chips offer outstanding new solutions that satisfy the increasing demand for high resolution and high quality video compression technologies such as H.264. Such solutions not only provide exceptional quality but also efficiency, low power, and low latency, previously unattainable in software based designs. While custom hardware and Application Specific Integrated Circuit (ASIC) technologies may achieve lowlatency, low power, and real-time performance in some consumer devices, many applications require a flexible and scalable software-defined solution. The deblocking filter in H.264 encoder/decoder poses difficult implementation challenges because of heavy data dependencies and the conditional nature of the computations. Deblocking filter implementations tend to be fixed and difficult to reconfigure for different needs. The ability to scale up for higher quality requirements such as 10-bit pixel depth or a 4:2:2 chroma format often reduces the throughput of a parallel architecture designed for lower feature set. A scalable architecture for deblocking filtering, created with a massively parallel processor based solution, means that the same encoder or decoder will be deployed in a variety of applications, at different video resolutions, for different power requirements, and at higher bit-depths and better color sub sampling patterns like YUV, 4:2:2, or 4:4:4 formats. Low power, software-defined encoders/decoders may be implemented using a massively parallel processor array, like that found in HyperX technology, with 100 or more cores and distributed memory. The large number of processor elements allows the silicon device to operate more efficiently than conventional DSP or CPU technology. This software programing model for massively parallel processors offers a flexible implementation and a power efficiency close to that of ASIC solutions. This work describes a scalable parallel architecture for an H.264 compliant deblocking filter for multi core platforms such as HyperX technology. Parallel techniques such as parallel processing of independent macroblocks, sub blocks, and pixel row level are examined in this work. The deblocking architecture consists of a basic cell called deblocking filter unit (DFU) and dependent data buffer manager (DFM). The DFU can be used in several instances, catering to different performance needs the DFM serves the data required for the different number of DFUs, and also manages all the neighboring data required for future data processing of DFUs. This approach achieves the scalability, flexibility, and performance excellence required in deblocking filters.

Acceleration of Semiempirical QM/MM Methods through Message Passage Interface (MPI), Hybrid MPI/Open Multiprocessing, and Self-Consistent Field Accelerator Implementations.

PubMed

Ojeda-May, Pedro; Nam, Kwangho

2017-08-08

The strategy and implementation of scalable and efficient semiempirical (SE) QM/MM methods in CHARMM are described. The serial version of the code was first profiled to identify routines that required parallelization. Afterward, the code was parallelized and accelerated with three approaches. The first approach was the parallelization of the entire QM/MM routines, including the Fock matrix diagonalization routines, using the CHARMM message passage interface (MPI) machinery. In the second approach, two different self-consistent field (SCF) energy convergence accelerators were implemented using density and Fock matrices as targets for their extrapolations in the SCF procedure. In the third approach, the entire QM/MM and MM energy routines were accelerated by implementing the hybrid MPI/open multiprocessing (OpenMP) model in which both the task- and loop-level parallelization strategies were adopted to balance loads between different OpenMP threads. The present implementation was tested on two solvated enzyme systems (including <100 QM atoms) and an S N 2 symmetric reaction in water. The MPI version exceeded existing SE QM methods in CHARMM, which include the SCC-DFTB and SQUANTUM methods, by at least 4-fold. The use of SCF convergence accelerators further accelerated the code by ∼12-35% depending on the size of the QM region and the number of CPU cores used. Although the MPI version displayed good scalability, the performance was diminished for large numbers of MPI processes due to the overhead associated with MPI communications between nodes. This issue was partially overcome by the hybrid MPI/OpenMP approach which displayed a better scalability for a larger number of CPU cores (up to 64 CPUs in the tested systems).

Scalable Implementation of Finite Elements by NASA _ Implicit (ScIFEi)

NASA Technical Reports Server (NTRS)

Warner, James E.; Bomarito, Geoffrey F.; Heber, Gerd; Hochhalter, Jacob D.

2016-01-01

Scalable Implementation of Finite Elements by NASA (ScIFEN) is a parallel finite element analysis code written in C++. ScIFEN is designed to provide scalable solutions to computational mechanics problems. It supports a variety of finite element types, nonlinear material models, and boundary conditions. This report provides an overview of ScIFEi (\\Sci-Fi"), the implicit solid mechanics driver within ScIFEN. A description of ScIFEi's capabilities is provided, including an overview of the tools and features that accompany the software as well as a description of the input and output le formats. Results from several problems are included, demonstrating the efficiency and scalability of ScIFEi by comparing to finite element analysis using a commercial code.

Parallel processing implementation for the coupled transport of photons and electrons using OpenMP

NASA Astrophysics Data System (ADS)

Doerner, Edgardo

2016-05-01

In this work the use of OpenMP to implement the parallel processing of the Monte Carlo (MC) simulation of the coupled transport for photons and electrons is presented. This implementation was carried out using a modified EGSnrc platform which enables the use of the Microsoft Visual Studio 2013 (VS2013) environment, together with the developing tools available in the Intel Parallel Studio XE 2015 (XE2015). The performance study of this new implementation was carried out in a desktop PC with a multi-core CPU, taking as a reference the performance of the original platform. The results were satisfactory, both in terms of scalability as parallelization efficiency.

A Massively Parallel Code for Polarization Calculations

NASA Astrophysics Data System (ADS)

Akiyama, Shizuka; Höflich, Peter

2001-03-01

We present an implementation of our Monte-Carlo radiation transport method for rapidly expanding, NLTE atmospheres for massively parallel computers which utilizes both the distributed and shared memory models. This allows us to take full advantage of the fast communication and low latency inherent to nodes with multiple CPUs, and to stretch the limits of scalability with the number of nodes compared to a version which is based on the shared memory model. Test calculations on a local 20-node Beowulf cluster with dual CPUs showed an improved scalability by about 40%.

Scalable Unix tools on parallel processors

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gropp, W.; Lusk, E.

1994-12-31

The introduction of parallel processors that run a separate copy of Unix on each process has introduced new problems in managing the user`s environment. This paper discusses some generalizations of common Unix commands for managing files (e.g. 1s) and processes (e.g. ps) that are convenient and scalable. These basic tools, just like their Unix counterparts, are text-based. We also discuss a way to use these with a graphical user interface (GUI). Some notes on the implementation are provided. Prototypes of these commands are publicly available.

A scalable parallel algorithm for multiple objective linear programs

NASA Technical Reports Server (NTRS)

Wiecek, Malgorzata M.; Zhang, Hong

1994-01-01

This paper presents an ADBASE-based parallel algorithm for solving multiple objective linear programs (MOLP's). Job balance, speedup and scalability are of primary interest in evaluating efficiency of the new algorithm. Implementation results on Intel iPSC/2 and Paragon multiprocessors show that the algorithm significantly speeds up the process of solving MOLP's, which is understood as generating all or some efficient extreme points and unbounded efficient edges. The algorithm gives specially good results for large and very large problems. Motivation and justification for solving such large MOLP's are also included.

Novel Scalable 3-D MT Inverse Solver

NASA Astrophysics Data System (ADS)

Kuvshinov, A. V.; Kruglyakov, M.; Geraskin, A.

2016-12-01

We present a new, robust and fast, three-dimensional (3-D) magnetotelluric (MT) inverse solver. As a forward modelling engine a highly-scalable solver extrEMe [1] is used. The (regularized) inversion is based on an iterative gradient-type optimization (quasi-Newton method) and exploits adjoint sources approach for fast calculation of the gradient of the misfit. The inverse solver is able to deal with highly detailed and contrasting models, allows for working (separately or jointly) with any type of MT (single-site and/or inter-site) responses, and supports massive parallelization. Different parallelization strategies implemented in the code allow for optimal usage of available computational resources for a given problem set up. To parameterize an inverse domain a mask approach is implemented, which means that one can merge any subset of forward modelling cells in order to account for (usually) irregular distribution of observation sites. We report results of 3-D numerical experiments aimed at analysing the robustness, performance and scalability of the code. In particular, our computational experiments carried out at different platforms ranging from modern laptops to high-performance clusters demonstrate practically linear scalability of the code up to thousands of nodes. 1. Kruglyakov, M., A. Geraskin, A. Kuvshinov, 2016. Novel accurate and scalable 3-D MT forward solver based on a contracting integral equation method, Computers and Geosciences, in press.

Language Classification using N-grams Accelerated by FPGA-based Bloom Filters

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jacob, A; Gokhale, M

N-Gram (n-character sequences in text documents) counting is a well-established technique used in classifying the language of text in a document. In this paper, n-gram processing is accelerated through the use of reconfigurable hardware on the XtremeData XD1000 system. Our design employs parallelism at multiple levels, with parallel Bloom Filters accessing on-chip RAM, parallel language classifiers, and parallel document processing. In contrast to another hardware implementation (HAIL algorithm) that uses off-chip SRAM for lookup, our highly scalable implementation uses only on-chip memory blocks. Our implementation of end-to-end language classification runs at 85x comparable software and 1.45x the competing hardware design.

A Robust and Scalable Software Library for Parallel Adaptive Refinement on Unstructured Meshes

NASA Technical Reports Server (NTRS)

Lou, John Z.; Norton, Charles D.; Cwik, Thomas A.

1999-01-01

The design and implementation of Pyramid, a software library for performing parallel adaptive mesh refinement (PAMR) on unstructured meshes, is described. This software library can be easily used in a variety of unstructured parallel computational applications, including parallel finite element, parallel finite volume, and parallel visualization applications using triangular or tetrahedral meshes. The library contains a suite of well-designed and efficiently implemented modules that perform operations in a typical PAMR process. Among these are mesh quality control during successive parallel adaptive refinement (typically guided by a local-error estimator), parallel load-balancing, and parallel mesh partitioning using the ParMeTiS partitioner. The Pyramid library is implemented in Fortran 90 with an interface to the Message-Passing Interface (MPI) library, supporting code efficiency, modularity, and portability. An EM waveguide filter application, adaptively refined using the Pyramid library, is illustrated.

Execution of parallel algorithms on a heterogeneous multicomputer

NASA Astrophysics Data System (ADS)

Isenstein, Barry S.; Greene, Jonathon

1995-04-01

Many aerospace/defense sensing and dual-use applications require high-performance computing, extensive high-bandwidth interconnect and realtime deterministic operation. This paper will describe the architecture of a scalable multicomputer that includes DSP and RISC processors. A single chassis implementation is capable of delivering in excess of 10 GFLOPS of DSP processing power with 2 Gbytes/s of realtime sensor I/O. A software approach to implementing parallel algorithms called the Parallel Application System (PAS) is also presented. An example of applying PAS to a DSP application is shown.

The novel high-performance 3-D MT inverse solver

NASA Astrophysics Data System (ADS)

Kruglyakov, Mikhail; Geraskin, Alexey; Kuvshinov, Alexey

2016-04-01

We present novel, robust, scalable, and fast 3-D magnetotelluric (MT) inverse solver. The solver is written in multi-language paradigm to make it as efficient, readable and maintainable as possible. Separation of concerns and single responsibility concepts go through implementation of the solver. As a forward modelling engine a modern scalable solver extrEMe, based on contracting integral equation approach, is used. Iterative gradient-type (quasi-Newton) optimization scheme is invoked to search for (regularized) inverse problem solution, and adjoint source approach is used to calculate efficiently the gradient of the misfit. The inverse solver is able to deal with highly detailed and contrasting models, allows for working (separately or jointly) with any type of MT responses, and supports massive parallelization. Moreover, different parallelization strategies implemented in the code allow optimal usage of available computational resources for a given problem statement. To parameterize an inverse domain the so-called mask parameterization is implemented, which means that one can merge any subset of forward modelling cells in order to account for (usually) irregular distribution of observation sites. We report results of 3-D numerical experiments aimed at analysing the robustness, performance and scalability of the code. In particular, our computational experiments carried out at different platforms ranging from modern laptops to HPC Piz Daint (6th supercomputer in the world) demonstrate practically linear scalability of the code up to thousands of nodes.

Massively parallel and linear-scaling algorithm for second-order Møller-Plesset perturbation theory applied to the study of supramolecular wires

NASA Astrophysics Data System (ADS)

Kjærgaard, Thomas; Baudin, Pablo; Bykov, Dmytro; Eriksen, Janus Juul; Ettenhuber, Patrick; Kristensen, Kasper; Larkin, Jeff; Liakh, Dmitry; Pawłowski, Filip; Vose, Aaron; Wang, Yang Min; Jørgensen, Poul

2017-03-01

We present a scalable cross-platform hybrid MPI/OpenMP/OpenACC implementation of the Divide-Expand-Consolidate (DEC) formalism with portable performance on heterogeneous HPC architectures. The Divide-Expand-Consolidate formalism is designed to reduce the steep computational scaling of conventional many-body methods employed in electronic structure theory to linear scaling, while providing a simple mechanism for controlling the error introduced by this approximation. Our massively parallel implementation of this general scheme has three levels of parallelism, being a hybrid of the loosely coupled task-based parallelization approach and the conventional MPI +X programming model, where X is either OpenMP or OpenACC. We demonstrate strong and weak scalability of this implementation on heterogeneous HPC systems, namely on the GPU-based Cray XK7 Titan supercomputer at the Oak Ridge National Laboratory. Using the "resolution of the identity second-order Møller-Plesset perturbation theory" (RI-MP2) as the physical model for simulating correlated electron motion, the linear-scaling DEC implementation is applied to 1-aza-adamantane-trione (AAT) supramolecular wires containing up to 40 monomers (2440 atoms, 6800 correlated electrons, 24 440 basis functions and 91 280 auxiliary functions). This represents the largest molecular system treated at the MP2 level of theory, demonstrating an efficient removal of the scaling wall pertinent to conventional quantum many-body methods.

Manyscale Computing for Sensor Processing in Support of Space Situational Awareness

NASA Astrophysics Data System (ADS)

Schmalz, M.; Chapman, W.; Hayden, E.; Sahni, S.; Ranka, S.

2014-09-01

Increasing image and signal data burden associated with sensor data processing in support of space situational awareness implies continuing computational throughput growth beyond the petascale regime. In addition to growing applications data burden and diversity, the breadth, diversity and scalability of high performance computing architectures and their various organizations challenge the development of a single, unifying, practicable model of parallel computation. Therefore, models for scalable parallel processing have exploited architectural and structural idiosyncrasies, yielding potential misapplications when legacy programs are ported among such architectures. In response to this challenge, we have developed a concise, efficient computational paradigm and software called Manyscale Computing to facilitate efficient mapping of annotated application codes to heterogeneous parallel architectures. Our theory, algorithms, software, and experimental results support partitioning and scheduling of application codes for envisioned parallel architectures, in terms of work atoms that are mapped (for example) to threads or thread blocks on computational hardware. Because of the rigor, completeness, conciseness, and layered design of our manyscale approach, application-to-architecture mapping is feasible and scalable for architectures at petascales, exascales, and above. Further, our methodology is simple, relying primarily on a small set of primitive mapping operations and support routines that are readily implemented on modern parallel processors such as graphics processing units (GPUs) and hybrid multi-processors (HMPs). In this paper, we overview the opportunities and challenges of manyscale computing for image and signal processing in support of space situational awareness applications. We discuss applications in terms of a layered hardware architecture (laboratory > supercomputer > rack > processor > component hierarchy). Demonstration applications include performance analysis and results in terms of execution time as well as storage, power, and energy consumption for bus-connected and/or networked architectures. The feasibility of the manyscale paradigm is demonstrated by addressing four principal challenges: (1) architectural/structural diversity, parallelism, and locality, (2) masking of I/O and memory latencies, (3) scalability of design as well as implementation, and (4) efficient representation/expression of parallel applications. Examples will demonstrate how manyscale computing helps solve these challenges efficiently on real-world computing systems.

Scalable Evaluation of Polarization Energy and Associated Forces in Polarizable Molecular Dynamics: II.Towards Massively Parallel Computations using Smooth Particle Mesh Ewald.

PubMed

Lagardère, Louis; Lipparini, Filippo; Polack, Étienne; Stamm, Benjamin; Cancès, Éric; Schnieders, Michael; Ren, Pengyu; Maday, Yvon; Piquemal, Jean-Philip

2014-02-28

In this paper, we present a scalable and efficient implementation of point dipole-based polarizable force fields for molecular dynamics (MD) simulations with periodic boundary conditions (PBC). The Smooth Particle-Mesh Ewald technique is combined with two optimal iterative strategies, namely, a preconditioned conjugate gradient solver and a Jacobi solver in conjunction with the Direct Inversion in the Iterative Subspace for convergence acceleration, to solve the polarization equations. We show that both solvers exhibit very good parallel performances and overall very competitive timings in an energy-force computation needed to perform a MD step. Various tests on large systems are provided in the context of the polarizable AMOEBA force field as implemented in the newly developed Tinker-HP package which is the first implementation for a polarizable model making large scale experiments for massively parallel PBC point dipole models possible. We show that using a large number of cores offers a significant acceleration of the overall process involving the iterative methods within the context of spme and a noticeable improvement of the memory management giving access to very large systems (hundreds of thousands of atoms) as the algorithm naturally distributes the data on different cores. Coupled with advanced MD techniques, gains ranging from 2 to 3 orders of magnitude in time are now possible compared to non-optimized, sequential implementations giving new directions for polarizable molecular dynamics in periodic boundary conditions using massively parallel implementations.

Scalable Evaluation of Polarization Energy and Associated Forces in Polarizable Molecular Dynamics: II.Towards Massively Parallel Computations using Smooth Particle Mesh Ewald

PubMed Central

Lagardère, Louis; Lipparini, Filippo; Polack, Étienne; Stamm, Benjamin; Cancès, Éric; Schnieders, Michael; Ren, Pengyu; Maday, Yvon; Piquemal, Jean-Philip

2015-01-01

In this paper, we present a scalable and efficient implementation of point dipole-based polarizable force fields for molecular dynamics (MD) simulations with periodic boundary conditions (PBC). The Smooth Particle-Mesh Ewald technique is combined with two optimal iterative strategies, namely, a preconditioned conjugate gradient solver and a Jacobi solver in conjunction with the Direct Inversion in the Iterative Subspace for convergence acceleration, to solve the polarization equations. We show that both solvers exhibit very good parallel performances and overall very competitive timings in an energy-force computation needed to perform a MD step. Various tests on large systems are provided in the context of the polarizable AMOEBA force field as implemented in the newly developed Tinker-HP package which is the first implementation for a polarizable model making large scale experiments for massively parallel PBC point dipole models possible. We show that using a large number of cores offers a significant acceleration of the overall process involving the iterative methods within the context of spme and a noticeable improvement of the memory management giving access to very large systems (hundreds of thousands of atoms) as the algorithm naturally distributes the data on different cores. Coupled with advanced MD techniques, gains ranging from 2 to 3 orders of magnitude in time are now possible compared to non-optimized, sequential implementations giving new directions for polarizable molecular dynamics in periodic boundary conditions using massively parallel implementations. PMID:26512230

Scalability and Portability of Two Parallel Implementations of ADI

NASA Technical Reports Server (NTRS)

Phung, Thanh; VanderWijngaart, Rob F.

1994-01-01

Two domain decompositions for the implementation of the NAS Scalar Penta-diagonal Parallel Benchmark on MIMD systems are investigated, namely transposition and multi-partitioning. Hardware platforms considered are the Intel iPSC/860 and Paragon XP/S-15, and clusters of SGI workstations on ethernet, communicating through PVM. It is found that the multi-partitioning strategy offers the kind of coarse granularity that allows scaling up to hundreds of processors on a massively parallel machine. Moreover, efficiency is retained when the code is ported verbatim (save message passing syntax) to a PVM environment on a modest size cluster of workstations.

A comprehensive study of MPI parallelism in three-dimensional discrete element method (DEM) simulation of complex-shaped granular particles

NASA Astrophysics Data System (ADS)

Yan, Beichuan; Regueiro, Richard A.

2018-02-01

A three-dimensional (3D) DEM code for simulating complex-shaped granular particles is parallelized using message-passing interface (MPI). The concepts of link-block, ghost/border layer, and migration layer are put forward for design of the parallel algorithm, and theoretical scalability function of 3-D DEM scalability and memory usage is derived. Many performance-critical implementation details are managed optimally to achieve high performance and scalability, such as: minimizing communication overhead, maintaining dynamic load balance, handling particle migrations across block borders, transmitting C++ dynamic objects of particles between MPI processes efficiently, eliminating redundant contact information between adjacent MPI processes. The code executes on multiple US Department of Defense (DoD) supercomputers and tests up to 2048 compute nodes for simulating 10 million three-axis ellipsoidal particles. Performance analyses of the code including speedup, efficiency, scalability, and granularity across five orders of magnitude of simulation scale (number of particles) are provided, and they demonstrate high speedup and excellent scalability. It is also discovered that communication time is a decreasing function of the number of compute nodes in strong scaling measurements. The code's capability of simulating a large number of complex-shaped particles on modern supercomputers will be of value in both laboratory studies on micromechanical properties of granular materials and many realistic engineering applications involving granular materials.

Multi-jagged: A scalable parallel spatial partitioning algorithm

DOE PAGES

Deveci, Mehmet; Rajamanickam, Sivasankaran; Devine, Karen D.; ...

2015-03-18

Geometric partitioning is fast and effective for load-balancing dynamic applications, particularly those requiring geometric locality of data (particle methods, crash simulations). We present, to our knowledge, the first parallel implementation of a multidimensional-jagged geometric partitioner. In contrast to the traditional recursive coordinate bisection algorithm (RCB), which recursively bisects subdomains perpendicular to their longest dimension until the desired number of parts is obtained, our algorithm does recursive multi-section with a given number of parts in each dimension. By computing multiple cut lines concurrently and intelligently deciding when to migrate data while computing the partition, we minimize data movement compared to efficientmore » implementations of recursive bisection. We demonstrate the algorithm's scalability and quality relative to the RCB implementation in Zoltan on both real and synthetic datasets. Our experiments show that the proposed algorithm performs and scales better than RCB in terms of run-time without degrading the load balance. Lastly, our implementation partitions 24 billion points into 65,536 parts within a few seconds and exhibits near perfect weak scaling up to 6K cores.« less

A Scalable Implementation of Van der Waals Density Functionals

NASA Astrophysics Data System (ADS)

Wu, Jun; Gygi, Francois

2010-03-01

Recently developed Van der Waals density functionals[1] offer the promise to account for weak intermolecular interactions that are not described accurately by local exchange-correlation density functionals. In spite of recent progress [2], the computational cost of such calculations remains high. We present a scalable parallel implementation of the functional proposed by Dion et al.[1]. The method is implemented in the Qbox first-principles simulation code (http://eslab.ucdavis.edu/software/qbox). Application to large molecular systems will be presented. [4pt] [1] M. Dion et al. Phys. Rev. Lett. 92, 246401 (2004).[0pt] [2] G. Roman-Perez and J. M. Soler, Phys. Rev. Lett. 103, 096102 (2009).

Execution of a parallel edge-based Navier-Stokes solver on commodity graphics processor units

NASA Astrophysics Data System (ADS)

Corral, Roque; Gisbert, Fernando; Pueblas, Jesus

2017-02-01

The implementation of an edge-based three-dimensional Reynolds Average Navier-Stokes solver for unstructured grids able to run on multiple graphics processing units (GPUs) is presented. Loops over edges, which are the most time-consuming part of the solver, have been written to exploit the massively parallel capabilities of GPUs. Non-blocking communications between parallel processes and between the GPU and the central processor unit (CPU) have been used to enhance code scalability. The code is written using a mixture of C++ and OpenCL, to allow the execution of the source code on GPUs. The Message Passage Interface (MPI) library is used to allow the parallel execution of the solver on multiple GPUs. A comparative study of the solver parallel performance is carried out using a cluster of CPUs and another of GPUs. It is shown that a single GPU is up to 64 times faster than a single CPU core. The parallel scalability of the solver is mainly degraded due to the loss of computing efficiency of the GPU when the size of the case decreases. However, for large enough grid sizes, the scalability is strongly improved. A cluster featuring commodity GPUs and a high bandwidth network is ten times less costly and consumes 33% less energy than a CPU-based cluster with an equivalent computational power.

Enhancing Scalability and Efficiency of the TOUGH2_MP for LinuxClusters

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhang, Keni; Wu, Yu-Shu

2006-04-17

TOUGH2{_}MP, the parallel version TOUGH2 code, has been enhanced by implementing more efficient communication schemes. This enhancement is achieved through reducing the amount of small-size messages and the volume of large messages. The message exchange speed is further improved by using non-blocking communications for both linear and nonlinear iterations. In addition, we have modified the AZTEC parallel linear-equation solver to nonblocking communication. Through the improvement of code structuring and bug fixing, the new version code is now more stable, while demonstrating similar or even better nonlinear iteration converging speed than the original TOUGH2 code. As a result, the new versionmore » of TOUGH2{_}MP is improved significantly in its efficiency. In this paper, the scalability and efficiency of the parallel code are demonstrated by solving two large-scale problems. The testing results indicate that speedup of the code may depend on both problem size and complexity. In general, the code has excellent scalability in memory requirement as well as computing time.« less

Three-dimensional Finite Element Formulation and Scalable Domain Decomposition for High Fidelity Rotor Dynamic Analysis

NASA Technical Reports Server (NTRS)

Datta, Anubhav; Johnson, Wayne R.

2009-01-01

This paper has two objectives. The first objective is to formulate a 3-dimensional Finite Element Model for the dynamic analysis of helicopter rotor blades. The second objective is to implement and analyze a dual-primal iterative substructuring based Krylov solver, that is parallel and scalable, for the solution of the 3-D FEM analysis. The numerical and parallel scalability of the solver is studied using two prototype problems - one for ideal hover (symmetric) and one for a transient forward flight (non-symmetric) - both carried out on up to 48 processors. In both hover and forward flight conditions, a perfect linear speed-up is observed, for a given problem size, up to the point of substructure optimality. Substructure optimality and the linear parallel speed-up range are both shown to depend on the problem size as well as on the selection of the coarse problem. With a larger problem size, linear speed-up is restored up to the new substructure optimality. The solver also scales with problem size - even though this conclusion is premature given the small prototype grids considered in this study.

Efficient Parallel Levenberg-Marquardt Model Fitting towards Real-Time Automated Parametric Imaging Microscopy

PubMed Central

Zhu, Xiang; Zhang, Dianwen

2013-01-01

We present a fast, accurate and robust parallel Levenberg-Marquardt minimization optimizer, GPU-LMFit, which is implemented on graphics processing unit for high performance scalable parallel model fitting processing. GPU-LMFit can provide a dramatic speed-up in massive model fitting analyses to enable real-time automated pixel-wise parametric imaging microscopy. We demonstrate the performance of GPU-LMFit for the applications in superresolution localization microscopy and fluorescence lifetime imaging microscopy. PMID:24130785

A distributed parallel storage architecture and its potential application within EOSDIS

NASA Technical Reports Server (NTRS)

Johnston, William E.; Tierney, Brian; Feuquay, Jay; Butzer, Tony

1994-01-01

We describe the architecture, implementation, use of a scalable, high performance, distributed-parallel data storage system developed in the ARPA funded MAGIC gigabit testbed. A collection of wide area distributed disk servers operate in parallel to provide logical block level access to large data sets. Operated primarily as a network-based cache, the architecture supports cooperation among independently owned resources to provide fast, large-scale, on-demand storage to support data handling, simulation, and computation.

Parallel implementation of a Lagrangian-based model on an adaptive mesh in C++: Application to sea-ice

NASA Astrophysics Data System (ADS)

Samaké, Abdoulaye; Rampal, Pierre; Bouillon, Sylvain; Ólason, Einar

2017-12-01

We present a parallel implementation framework for a new dynamic/thermodynamic sea-ice model, called neXtSIM, based on the Elasto-Brittle rheology and using an adaptive mesh. The spatial discretisation of the model is done using the finite-element method. The temporal discretisation is semi-implicit and the advection is achieved using either a pure Lagrangian scheme or an Arbitrary Lagrangian Eulerian scheme (ALE). The parallel implementation presented here focuses on the distributed-memory approach using the message-passing library MPI. The efficiency and the scalability of the parallel algorithms are illustrated by the numerical experiments performed using up to 500 processor cores of a cluster computing system. The performance obtained by the proposed parallel implementation of the neXtSIM code is shown being sufficient to perform simulations for state-of-the-art sea ice forecasting and geophysical process studies over geographical domain of several millions squared kilometers like the Arctic region.

XDATA

DTIC Science & Technology

2017-05-01

Parallelizing PINT The main focus of our research into the parallelization of the PINT algorithm has been to find appropriately scalable matrix math algorithms...leading eigenvector of the adjacency matrix of the pairwise affinity graph. We reviewed the matrix math implementation currently being used in PINT and...the new versions support a feature called matrix.distributed, which is some level of support for distributed matrix math ; however our code is not

Performance evaluation of OpenFOAM on many-core architectures

DOE Office of Scientific and Technical Information (OSTI.GOV)

Brzobohatý, Tomáš; Říha, Lubomír; Karásek, Tomáš, E-mail: tomas.karasek@vsb.cz

In this article application of Open Source Field Operation and Manipulation (OpenFOAM) C++ libraries for solving engineering problems on many-core architectures is presented. Objective of this article is to present scalability of OpenFOAM on parallel platforms solving real engineering problems of fluid dynamics. Scalability test of OpenFOAM is performed using various hardware and different implementation of standard PCG and PBiCG Krylov iterative methods. Speed up of various implementations of linear solvers using GPU and MIC accelerators are presented in this paper. Numerical experiments of 3D lid-driven cavity flow for several cases with various number of cells are presented.

Efficient Parallelization of a Dynamic Unstructured Application on the Tera MTA

NASA Technical Reports Server (NTRS)

Oliker, Leonid; Biswas, Rupak

1999-01-01

The success of parallel computing in solving real-life computationally-intensive problems relies on their efficient mapping and execution on large-scale multiprocessor architectures. Many important applications are both unstructured and dynamic in nature, making their efficient parallel implementation a daunting task. This paper presents the parallelization of a dynamic unstructured mesh adaptation algorithm using three popular programming paradigms on three leading supercomputers. We examine an MPI message-passing implementation on the Cray T3E and the SGI Origin2OOO, a shared-memory implementation using cache coherent nonuniform memory access (CC-NUMA) of the Origin2OOO, and a multi-threaded version on the newly-released Tera Multi-threaded Architecture (MTA). We compare several critical factors of this parallel code development, including runtime, scalability, programmability, and memory overhead. Our overall results demonstrate that multi-threaded systems offer tremendous potential for quickly and efficiently solving some of the most challenging real-life problems on parallel computers.

Large-scale virtual screening on public cloud resources with Apache Spark.

PubMed

Capuccini, Marco; Ahmed, Laeeq; Schaal, Wesley; Laure, Erwin; Spjuth, Ola

2017-01-01

Structure-based virtual screening is an in-silico method to screen a target receptor against a virtual molecular library. Applying docking-based screening to large molecular libraries can be computationally expensive, however it constitutes a trivially parallelizable task. Most of the available parallel implementations are based on message passing interface, relying on low failure rate hardware and fast network connection. Google's MapReduce revolutionized large-scale analysis, enabling the processing of massive datasets on commodity hardware and cloud resources, providing transparent scalability and fault tolerance at the software level. Open source implementations of MapReduce include Apache Hadoop and the more recent Apache Spark. We developed a method to run existing docking-based screening software on distributed cloud resources, utilizing the MapReduce approach. We benchmarked our method, which is implemented in Apache Spark, docking a publicly available target receptor against [Formula: see text]2.2 M compounds. The performance experiments show a good parallel efficiency (87%) when running in a public cloud environment. Our method enables parallel Structure-based virtual screening on public cloud resources or commodity computer clusters. The degree of scalability that we achieve allows for trying out our method on relatively small libraries first and then to scale to larger libraries. Our implementation is named Spark-VS and it is freely available as open source from GitHub (https://github.com/mcapuccini/spark-vs).Graphical abstract.

A Scalable Software Architecture Booting and Configuring Nodes in the Whitney Commodity Computing Testbed

NASA Technical Reports Server (NTRS)

Fineberg, Samuel A.; Kutler, Paul (Technical Monitor)

1997-01-01

The Whitney project is integrating commodity off-the-shelf PC hardware and software technology to build a parallel supercomputer with hundreds to thousands of nodes. To build such a system, one must have a scalable software model, and the installation and maintenance of the system software must be completely automated. We describe the design of an architecture for booting, installing, and configuring nodes in such a system with particular consideration given to scalability and ease of maintenance. This system has been implemented on a 40-node prototype of Whitney and is to be used on the 500 processor Whitney system to be built in 1998.

Mining algorithm for association rules in big data based on Hadoop

NASA Astrophysics Data System (ADS)

Fu, Chunhua; Wang, Xiaojing; Zhang, Lijun; Qiao, Liying

2018-04-01

In order to solve the problem that the traditional association rules mining algorithm has been unable to meet the mining needs of large amount of data in the aspect of efficiency and scalability, take FP-Growth as an example, the algorithm is realized in the parallelization based on Hadoop framework and Map Reduce model. On the basis, it is improved using the transaction reduce method for further enhancement of the algorithm's mining efficiency. The experiment, which consists of verification of parallel mining results, comparison on efficiency between serials and parallel, variable relationship between mining time and node number and between mining time and data amount, is carried out in the mining results and efficiency by Hadoop clustering. Experiments show that the paralleled FP-Growth algorithm implemented is able to accurately mine frequent item sets, with a better performance and scalability. It can be better to meet the requirements of big data mining and efficiently mine frequent item sets and association rules from large dataset.

Implementation of BT, SP, LU, and FT of NAS Parallel Benchmarks in Java

NASA Technical Reports Server (NTRS)

Schultz, Matthew; Frumkin, Michael; Jin, Hao-Qiang; Yan, Jerry

2000-01-01

A number of Java features make it an attractive but a debatable choice for High Performance Computing. We have implemented benchmarks working on single structured grid BT,SP,LU and FT in Java. The performance and scalability of the Java code shows that a significant improvement in Java compiler technology and in Java thread implementation are necessary for Java to compete with Fortran in HPC applications.

Joint Experiment on Scalable Parallel Processors (JESPP) Parallel Data Management

DTIC Science & Technology

2006-05-01

management and analysis tool, called Simulation Data Grid ( SDG ). The design principles driving the design of SDG are: 1) minimize network communication...or SDG . In this report, an initial prototype implementation of this system is described. This project follows on earlier research, primarily...distributed logging system had some 2 limitations. These limitations will be described in this report, and how the SDG addresses these limitations. 3.0

PARALLEL HOP: A SCALABLE HALO FINDER FOR MASSIVE COSMOLOGICAL DATA SETS

DOE Office of Scientific and Technical Information (OSTI.GOV)

Skory, Stephen; Turk, Matthew J.; Norman, Michael L.

2010-11-15

Modern N-body cosmological simulations contain billions (10{sup 9}) of dark matter particles. These simulations require hundreds to thousands of gigabytes of memory and employ hundreds to tens of thousands of processing cores on many compute nodes. In order to study the distribution of dark matter in a cosmological simulation, the dark matter halos must be identified using a halo finder, which establishes the halo membership of every particle in the simulation. The resources required for halo finding are similar to the requirements for the simulation itself. In particular, simulations have become too extensive to use commonly employed halo finders, suchmore » that the computational requirements to identify halos must now be spread across multiple nodes and cores. Here, we present a scalable-parallel halo finding method called Parallel HOP for large-scale cosmological simulation data. Based on the halo finder HOP, it utilizes message passing interface and domain decomposition to distribute the halo finding workload across multiple compute nodes, enabling analysis of much larger data sets than is possible with the strictly serial or previous parallel implementations of HOP. We provide a reference implementation of this method as a part of the toolkit {sup yt}, an analysis toolkit for adaptive mesh refinement data that include complementary analysis modules. Additionally, we discuss a suite of benchmarks that demonstrate that this method scales well up to several hundred tasks and data sets in excess of 2000{sup 3} particles. The Parallel HOP method and our implementation can be readily applied to any kind of N-body simulation data and is therefore widely applicable.« less

Cheetah: A Framework for Scalable Hierarchical Collective Operations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Graham, Richard L; Gorentla Venkata, Manjunath; Ladd, Joshua S

2011-01-01

Collective communication operations, used by many scientific applications, tend to limit overall parallel application performance and scalability. Computer systems are becoming more heterogeneous with increasing node and core-per-node counts. Also, a growing number of data-access mechanisms, of varying characteristics, are supported within a single computer system. We describe a new hierarchical collective communication framework that takes advantage of hardware-specific data-access mechanisms. It is flexible, with run-time hierarchy specification, and sharing of collective communication primitives between collective algorithms. Data buffers are shared between levels in the hierarchy reducing collective communication management overhead. We have implemented several versions of the Message Passingmore » Interface (MPI) collective operations, MPI Barrier() and MPI Bcast(), and run experiments using up to 49, 152 processes on a Cray XT5, and a small InfiniBand based cluster. At 49, 152 processes our barrier implementation outperforms the optimized native implementation by 75%. 32 Byte and one Mega-Byte broadcasts outperform it by 62% and 11%, respectively, with better scalability characteristics. Improvements relative to the default Open MPI implementation are much larger.« less

Implementation of a flexible and scalable particle-in-cell method for massively parallel computations in the mantle convection code ASPECT

NASA Astrophysics Data System (ADS)

Gassmöller, Rene; Bangerth, Wolfgang

2016-04-01

Particle-in-cell methods have a long history and many applications in geodynamic modelling of mantle convection, lithospheric deformation and crustal dynamics. They are primarily used to track material information, the strain a material has undergone, the pressure-temperature history a certain material region has experienced, or the amount of volatiles or partial melt present in a region. However, their efficient parallel implementation - in particular combined with adaptive finite-element meshes - is complicated due to the complex communication patterns and frequent reassignment of particles to cells. Consequently, many current scientific software packages accomplish this efficient implementation by specifically designing particle methods for a single purpose, like the advection of scalar material properties that do not evolve over time (e.g., for chemical heterogeneities). Design choices for particle integration, data storage, and parallel communication are then optimized for this single purpose, making the code relatively rigid to changing requirements. Here, we present the implementation of a flexible, scalable and efficient particle-in-cell method for massively parallel finite-element codes with adaptively changing meshes. Using a modular plugin structure, we allow maximum flexibility of the generation of particles, the carried tracer properties, the advection and output algorithms, and the projection of properties to the finite-element mesh. We present scaling tests ranging up to tens of thousands of cores and tens of billions of particles. Additionally, we discuss efficient load-balancing strategies for particles in adaptive meshes with their strengths and weaknesses, local particle-transfer between parallel subdomains utilizing existing communication patterns from the finite element mesh, and the use of established parallel output algorithms like the HDF5 library. Finally, we show some relevant particle application cases, compare our implementation to a modern advection-field approach, and demonstrate under which conditions which method is more efficient. We implemented the presented methods in ASPECT (aspect.dealii.org), a freely available open-source community code for geodynamic simulations. The structure of the particle code is highly modular, and segregated from the PDE solver, and can thus be easily transferred to other programs, or adapted for various application cases.

Time-dependent density-functional theory in massively parallel computer architectures: the octopus project

NASA Astrophysics Data System (ADS)

Andrade, Xavier; Alberdi-Rodriguez, Joseba; Strubbe, David A.; Oliveira, Micael J. T.; Nogueira, Fernando; Castro, Alberto; Muguerza, Javier; Arruabarrena, Agustin; Louie, Steven G.; Aspuru-Guzik, Alán; Rubio, Angel; Marques, Miguel A. L.

2012-06-01

Octopus is a general-purpose density-functional theory (DFT) code, with a particular emphasis on the time-dependent version of DFT (TDDFT). In this paper we present the ongoing efforts to achieve the parallelization of octopus. We focus on the real-time variant of TDDFT, where the time-dependent Kohn-Sham equations are directly propagated in time. This approach has great potential for execution in massively parallel systems such as modern supercomputers with thousands of processors and graphics processing units (GPUs). For harvesting the potential of conventional supercomputers, the main strategy is a multi-level parallelization scheme that combines the inherent scalability of real-time TDDFT with a real-space grid domain-partitioning approach. A scalable Poisson solver is critical for the efficiency of this scheme. For GPUs, we show how using blocks of Kohn-Sham states provides the required level of data parallelism and that this strategy is also applicable for code optimization on standard processors. Our results show that real-time TDDFT, as implemented in octopus, can be the method of choice for studying the excited states of large molecular systems in modern parallel architectures.

Time-dependent density-functional theory in massively parallel computer architectures: the OCTOPUS project.

PubMed

Andrade, Xavier; Alberdi-Rodriguez, Joseba; Strubbe, David A; Oliveira, Micael J T; Nogueira, Fernando; Castro, Alberto; Muguerza, Javier; Arruabarrena, Agustin; Louie, Steven G; Aspuru-Guzik, Alán; Rubio, Angel; Marques, Miguel A L

2012-06-13

Octopus is a general-purpose density-functional theory (DFT) code, with a particular emphasis on the time-dependent version of DFT (TDDFT). In this paper we present the ongoing efforts to achieve the parallelization of octopus. We focus on the real-time variant of TDDFT, where the time-dependent Kohn-Sham equations are directly propagated in time. This approach has great potential for execution in massively parallel systems such as modern supercomputers with thousands of processors and graphics processing units (GPUs). For harvesting the potential of conventional supercomputers, the main strategy is a multi-level parallelization scheme that combines the inherent scalability of real-time TDDFT with a real-space grid domain-partitioning approach. A scalable Poisson solver is critical for the efficiency of this scheme. For GPUs, we show how using blocks of Kohn-Sham states provides the required level of data parallelism and that this strategy is also applicable for code optimization on standard processors. Our results show that real-time TDDFT, as implemented in octopus, can be the method of choice for studying the excited states of large molecular systems in modern parallel architectures.

The Importance of Three-Body Interactions in Molecular Dynamics Simulations of Water with the Fragment Molecular Orbital Method

DOE Office of Scientific and Technical Information (OSTI.GOV)

Pruitt, Spencer R.; Nakata, Hiroya; Nagata, Takeshi

2016-04-12

The analytic first derivative with respect to nuclear coordinates is formulated and implemented in the framework of the three-body fragment molecular orbital (FMO) method. The gradient has been derived and implemented for restricted Hartree-Fock, second-order Møller-Plesset perturbation, and density functional theories. The importance of the three-body fully analytic gradient is illustrated through the failure of the two-body FMO method during molecular dynamics simulations of a small water cluster. The parallel implementation of the fragment molecular orbital method, its parallel efficiency, and its scalability on the Blue Gene/Q architecture up to 262,144 CPU cores, are also discussed.

libvdwxc: a library for exchange-correlation functionals in the vdW-DF family

NASA Astrophysics Data System (ADS)

Hjorth Larsen, Ask; Kuisma, Mikael; Löfgren, Joakim; Pouillon, Yann; Erhart, Paul; Hyldgaard, Per

2017-09-01

We present libvdwxc, a general library for evaluating the energy and potential for the family of vdW-DF exchange-correlation functionals. libvdwxc is written in C and provides an efficient implementation of the vdW-DF method and can be interfaced with various general-purpose DFT codes. Currently, the Gpaw and Octopus codes implement interfaces to libvdwxc. The present implementation emphasizes scalability and parallel performance, and thereby enables ab initio calculations of nanometer-scale complexes. The numerical accuracy is benchmarked on the S22 test set whereas parallel performance is benchmarked on ligand-protected gold nanoparticles ({{Au}}144{({{SC}}11{{NH}}25)}60) up to 9696 atoms.

Scalable and reusable emulator for evaluating the performance of SS7 networks

NASA Astrophysics Data System (ADS)

Lazar, Aurel A.; Tseng, Kent H.; Lim, Koon Seng; Choe, Winston

1994-04-01

A scalable and reusable emulator was designed and implemented for studying the behavior of SS7 networks. The emulator design was largely based on public domain software. It was developed on top of an environment supported by PVM, the Parallel Virtual Machine, and managed by OSIMIS-the OSI Management Information Service platform. The emulator runs on top of a commercially available ATM LAN interconnecting engineering workstations. As a case study for evaluating the emulator, the behavior of the Singapore National SS7 Network under fault and unbalanced loading conditions was investigated.

SeqPig: simple and scalable scripting for large sequencing data sets in Hadoop

PubMed Central

Schumacher, André; Pireddu, Luca; Niemenmaa, Matti; Kallio, Aleksi; Korpelainen, Eija; Zanetti, Gianluigi; Heljanko, Keijo

2014-01-01

Summary: Hadoop MapReduce-based approaches have become increasingly popular due to their scalability in processing large sequencing datasets. However, as these methods typically require in-depth expertise in Hadoop and Java, they are still out of reach of many bioinformaticians. To solve this problem, we have created SeqPig, a library and a collection of tools to manipulate, analyze and query sequencing datasets in a scalable and simple manner. SeqPigscripts use the Hadoop-based distributed scripting engine Apache Pig, which automatically parallelizes and distributes data processing tasks. We demonstrate SeqPig’s scalability over many computing nodes and illustrate its use with example scripts. Availability and Implementation: Available under the open source MIT license at http://sourceforge.net/projects/seqpig/ Contact: andre.schumacher@yahoo.com Supplementary information: Supplementary data are available at Bioinformatics online. PMID:24149054

Scalable hierarchical PDE sampler for generating spatially correlated random fields using nonmatching meshes: Scalable hierarchical PDE sampler using nonmatching meshes

DOE PAGES

Osborn, Sarah; Zulian, Patrick; Benson, Thomas; ...

2018-01-30

This work describes a domain embedding technique between two nonmatching meshes used for generating realizations of spatially correlated random fields with applications to large-scale sampling-based uncertainty quantification. The goal is to apply the multilevel Monte Carlo (MLMC) method for the quantification of output uncertainties of PDEs with random input coefficients on general and unstructured computational domains. We propose a highly scalable, hierarchical sampling method to generate realizations of a Gaussian random field on a given unstructured mesh by solving a reaction–diffusion PDE with a stochastic right-hand side. The stochastic PDE is discretized using the mixed finite element method on anmore » embedded domain with a structured mesh, and then, the solution is projected onto the unstructured mesh. This work describes implementation details on how to efficiently transfer data from the structured and unstructured meshes at coarse levels, assuming that this can be done efficiently on the finest level. We investigate the efficiency and parallel scalability of the technique for the scalable generation of Gaussian random fields in three dimensions. An application of the MLMC method is presented for quantifying uncertainties of subsurface flow problems. Here, we demonstrate the scalability of the sampling method with nonmatching mesh embedding, coupled with a parallel forward model problem solver, for large-scale 3D MLMC simulations with up to 1.9·109 unknowns.« less

Scalable hierarchical PDE sampler for generating spatially correlated random fields using nonmatching meshes: Scalable hierarchical PDE sampler using nonmatching meshes

DOE Office of Scientific and Technical Information (OSTI.GOV)

Osborn, Sarah; Zulian, Patrick; Benson, Thomas

This work describes a domain embedding technique between two nonmatching meshes used for generating realizations of spatially correlated random fields with applications to large-scale sampling-based uncertainty quantification. The goal is to apply the multilevel Monte Carlo (MLMC) method for the quantification of output uncertainties of PDEs with random input coefficients on general and unstructured computational domains. We propose a highly scalable, hierarchical sampling method to generate realizations of a Gaussian random field on a given unstructured mesh by solving a reaction–diffusion PDE with a stochastic right-hand side. The stochastic PDE is discretized using the mixed finite element method on anmore » embedded domain with a structured mesh, and then, the solution is projected onto the unstructured mesh. This work describes implementation details on how to efficiently transfer data from the structured and unstructured meshes at coarse levels, assuming that this can be done efficiently on the finest level. We investigate the efficiency and parallel scalability of the technique for the scalable generation of Gaussian random fields in three dimensions. An application of the MLMC method is presented for quantifying uncertainties of subsurface flow problems. Here, we demonstrate the scalability of the sampling method with nonmatching mesh embedding, coupled with a parallel forward model problem solver, for large-scale 3D MLMC simulations with up to 1.9·109 unknowns.« less

Scalable Algorithms for Parallel Discrete Event Simulation Systems in Multicore Environments

DTIC Science & Technology

2013-05-01

consolidated at the sender side. At the receiver side, the messages are deconsolidated and delivered to the appropriate thread. This approach bears some...Jiang, S. Kini, W. Yu, D. Buntinas, P. Wyckoff, and D. Panda . Performance comparison of mpi implementations over infiniband, myrinet and quadrics

Profiling and Improving I/O Performance of a Large-Scale Climate Scientific Application

NASA Technical Reports Server (NTRS)

Liu, Zhuo; Wang, Bin; Wang, Teng; Tian, Yuan; Xu, Cong; Wang, Yandong; Yu, Weikuan; Cruz, Carlos A.; Zhou, Shujia; Clune, Tom;

Computing Gröbner and Involutive Bases for Linear Systems of Difference Equations

NASA Astrophysics Data System (ADS)

Yanovich, Denis

2018-02-01

The computation of involutive bases and Gröbner bases for linear systems of difference equations is solved and its importance for physical and mathematical problems is discussed. The algorithm and issues concerning its implementation in C are presented and calculation times are compared with the competing programs. The paper ends with consideration on the parallel version of this implementation and its scalability.

A Massively Parallel Computational Method of Reading Index Files for SOAPsnv.

PubMed

Zhu, Xiaoqian; Peng, Shaoliang; Liu, Shaojie; Cui, Yingbo; Gu, Xiang; Gao, Ming; Fang, Lin; Fang, Xiaodong

2015-12-01

SOAPsnv is the software used for identifying the single nucleotide variation in cancer genes. However, its performance is yet to match the massive amount of data to be processed. Experiments reveal that the main performance bottleneck of SOAPsnv software is the pileup algorithm. The original pileup algorithm's I/O process is time-consuming and inefficient to read input files. Moreover, the scalability of the pileup algorithm is also poor. Therefore, we designed a new algorithm, named BamPileup, aiming to improve the performance of sequential read, and the new pileup algorithm implemented a parallel read mode based on index. Using this method, each thread can directly read the data start from a specific position. The results of experiments on the Tianhe-2 supercomputer show that, when reading data in a multi-threaded parallel I/O way, the processing time of algorithm is reduced to 3.9 s and the application program can achieve a speedup up to 100×. Moreover, the scalability of the new algorithm is also satisfying.

Parallel performance investigations of an unstructured mesh Navier-Stokes solver

NASA Technical Reports Server (NTRS)

Mavriplis, Dimitri J.

2000-01-01

A Reynolds-averaged Navier-Stokes solver based on unstructured mesh techniques for analysis of high-lift configurations is described. The method makes use of an agglomeration multigrid solver for convergence acceleration. Implicit line-smoothing is employed to relieve the stiffness associated with highly stretched meshes. A GMRES technique is also implemented to speed convergence at the expense of additional memory usage. The solver is cache efficient and fully vectorizable, and is parallelized using a two-level hybrid MPI-OpenMP implementation suitable for shared and/or distributed memory architectures, as well as clusters of shared memory machines. Convergence and scalability results are illustrated for various high-lift cases.

MPI implementation of PHOENICS: A general purpose computational fluid dynamics code

NASA Astrophysics Data System (ADS)

Simunovic, S.; Zacharia, T.; Baltas, N.; Spalding, D. B.

1995-03-01

PHOENICS is a suite of computational analysis programs that are used for simulation of fluid flow, heat transfer, and dynamical reaction processes. The parallel version of the solver EARTH for the Computational Fluid Dynamics (CFD) program PHOENICS has been implemented using Message Passing Interface (MPI) standard. Implementation of MPI version of PHOENICS makes this computational tool portable to a wide range of parallel machines and enables the use of high performance computing for large scale computational simulations. MPI libraries are available on several parallel architectures making the program usable across different architectures as well as on heterogeneous computer networks. The Intel Paragon NX and MPI versions of the program have been developed and tested on massively parallel supercomputers Intel Paragon XP/S 5, XP/S 35, and Kendall Square Research, and on the multiprocessor SGI Onyx computer at Oak Ridge National Laboratory. The preliminary testing results of the developed program have shown scalable performance for reasonably sized computational domains.

MPI implementation of PHOENICS: A general purpose computational fluid dynamics code

DOE Office of Scientific and Technical Information (OSTI.GOV)

Simunovic, S.; Zacharia, T.; Baltas, N.

1995-04-01

PHOENICS is a suite of computational analysis programs that are used for simulation of fluid flow, heat transfer, and dynamical reaction processes. The parallel version of the solver EARTH for the Computational Fluid Dynamics (CFD) program PHOENICS has been implemented using Message Passing Interface (MPI) standard. Implementation of MPI version of PHOENICS makes this computational tool portable to a wide range of parallel machines and enables the use of high performance computing for large scale computational simulations. MPI libraries are available on several parallel architectures making the program usable across different architectures as well as on heterogeneous computer networks. Themore » Intel Paragon NX and MPI versions of the program have been developed and tested on massively parallel supercomputers Intel Paragon XP/S 5, XP/S 35, and Kendall Square Research, and on the multiprocessor SGI Onyx computer at Oak Ridge National Laboratory. The preliminary testing results of the developed program have shown scalable performance for reasonably sized computational domains.« less

Full Parallel Implementation of an All-Electron Four-Component Dirac-Kohn-Sham Program.

PubMed

Rampino, Sergio; Belpassi, Leonardo; Tarantelli, Francesco; Storchi, Loriano

2014-09-09

A full distributed-memory implementation of the Dirac-Kohn-Sham (DKS) module of the program BERTHA (Belpassi et al., Phys. Chem. Chem. Phys. 2011, 13, 12368-12394) is presented, where the self-consistent field (SCF) procedure is replicated on all the parallel processes, each process working on subsets of the global matrices. The key feature of the implementation is an efficient procedure for switching between two matrix distribution schemes, one (integral-driven) optimal for the parallel computation of the matrix elements and another (block-cyclic) optimal for the parallel linear algebra operations. This approach, making both CPU-time and memory scalable with the number of processors used, virtually overcomes at once both time and memory barriers associated with DKS calculations. Performance, portability, and numerical stability of the code are illustrated on the basis of test calculations on three gold clusters of increasing size, an organometallic compound, and a perovskite model. The calculations are performed on a Beowulf and a BlueGene/Q system.

Evolution of a minimal parallel programming model

DOE PAGES

Lusk, Ewing; Butler, Ralph; Pieper, Steven C.

2017-04-30

Here, we take a historical approach to our presentation of self-scheduled task parallelism, a programming model with its origins in early irregular and nondeterministic computations encountered in automated theorem proving and logic programming. We show how an extremely simple task model has evolved into a system, asynchronous dynamic load balancing (ADLB), and a scalable implementation capable of supporting sophisticated applications on today’s (and tomorrow’s) largest supercomputers; and we illustrate the use of ADLB with a Green’s function Monte Carlo application, a modern, mature nuclear physics code in production use. Our lesson is that by surrendering a certain amount of generalitymore » and thus applicability, a minimal programming model (in terms of its basic concepts and the size of its application programmer interface) can achieve extreme scalability without introducing complexity.« less

Applications and accuracy of the parallel diagonal dominant algorithm

NASA Technical Reports Server (NTRS)

Sun, Xian-He

1993-01-01

The Parallel Diagonal Dominant (PDD) algorithm is a highly efficient, ideally scalable tridiagonal solver. In this paper, a detailed study of the PDD algorithm is given. First the PDD algorithm is introduced. Then the algorithm is extended to solve periodic tridiagonal systems. A variant, the reduced PDD algorithm, is also proposed. Accuracy analysis is provided for a class of tridiagonal systems, the symmetric, and anti-symmetric Toeplitz tridiagonal systems. Implementation results show that the analysis gives a good bound on the relative error, and the algorithm is a good candidate for the emerging massively parallel machines.

Linux Kernel Co-Scheduling For Bulk Synchronous Parallel Applications

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jones, Terry R

2011-01-01

This paper describes a kernel scheduling algorithm that is based on co-scheduling principles and that is intended for parallel applications running on 1000 cores or more where inter-node scalability is key. Experimental results for a Linux implementation on a Cray XT5 machine are presented.1 The results indicate that Linux is a suitable operating system for this new scheduling scheme, and that this design provides a dramatic improvement in scaling performance for synchronizing collective operations at scale.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yu, Weikuan; Vetter, Jeffrey S

Parallel NFS (pNFS) is touted as an emergent standard protocol for parallel I/O access in various storage environments. Several pNFS prototypes have been implemented for initial validation and protocol examination. Previous efforts have focused on realizing the pNFS protocol to expose the best bandwidth potential from underlying file and storage systems. In this presentation, we provide an initial characterization of two pNFS prototype implementations, lpNFS (a Lustre-based parallel NFS implementation) and spNFS (another reference implementation from Network Appliance, Inc.). We show that both lpNFS and spNFS can faithfully achieve the primary goal of pNFS, i.e., aggregating I/O bandwidth from manymore » storage servers. However, they both face the challenge of scalable metadata management. Particularly, the throughput of sp-NFS metadata operations degrades significanlty with an increasing number of data servers. Even for the better-performing lpNFS, we discuss its architecture and propose a direct I/O request flow protocol to improve its performance.« less

Hi-Corrector: a fast, scalable and memory-efficient package for normalizing large-scale Hi-C data.

PubMed

Li, Wenyuan; Gong, Ke; Li, Qingjiao; Alber, Frank; Zhou, Xianghong Jasmine

2015-03-15

Genome-wide proximity ligation assays, e.g. Hi-C and its variant TCC, have recently become important tools to study spatial genome organization. Removing biases from chromatin contact matrices generated by such techniques is a critical preprocessing step of subsequent analyses. The continuing decline of sequencing costs has led to an ever-improving resolution of the Hi-C data, resulting in very large matrices of chromatin contacts. Such large-size matrices, however, pose a great challenge on the memory usage and speed of its normalization. Therefore, there is an urgent need for fast and memory-efficient methods for normalization of Hi-C data. We developed Hi-Corrector, an easy-to-use, open source implementation of the Hi-C data normalization algorithm. Its salient features are (i) scalability-the software is capable of normalizing Hi-C data of any size in reasonable times; (ii) memory efficiency-the sequential version can run on any single computer with very limited memory, no matter how little; (iii) fast speed-the parallel version can run very fast on multiple computing nodes with limited local memory. The sequential version is implemented in ANSI C and can be easily compiled on any system; the parallel version is implemented in ANSI C with the MPI library (a standardized and portable parallel environment designed for solving large-scale scientific problems). The package is freely available at http://zhoulab.usc.edu/Hi-Corrector/. © The Author 2014. Published by Oxford University Press.

A parallel Monte Carlo code for planar and SPECT imaging: implementation, verification and applications in (131)I SPECT.

PubMed

Dewaraja, Yuni K; Ljungberg, Michael; Majumdar, Amitava; Bose, Abhijit; Koral, Kenneth F

2002-02-01

This paper reports the implementation of the SIMIND Monte Carlo code on an IBM SP2 distributed memory parallel computer. Basic aspects of running Monte Carlo particle transport calculations on parallel architectures are described. Our parallelization is based on equally partitioning photons among the processors and uses the Message Passing Interface (MPI) library for interprocessor communication and the Scalable Parallel Random Number Generator (SPRNG) to generate uncorrelated random number streams. These parallelization techniques are also applicable to other distributed memory architectures. A linear increase in computing speed with the number of processors is demonstrated for up to 32 processors. This speed-up is especially significant in Single Photon Emission Computed Tomography (SPECT) simulations involving higher energy photon emitters, where explicit modeling of the phantom and collimator is required. For (131)I, the accuracy of the parallel code is demonstrated by comparing simulated and experimental SPECT images from a heart/thorax phantom. Clinically realistic SPECT simulations using the voxel-man phantom are carried out to assess scatter and attenuation correction.

Large-scale molecular dynamics simulation of DNA: implementation and validation of the AMBER98 force field in LAMMPS.

PubMed

Grindon, Christina; Harris, Sarah; Evans, Tom; Novik, Keir; Coveney, Peter; Laughton, Charles

2004-07-15

Molecular modelling played a central role in the discovery of the structure of DNA by Watson and Crick. Today, such modelling is done on computers: the more powerful these computers are, the more detailed and extensive can be the study of the dynamics of such biological macromolecules. To fully harness the power of modern massively parallel computers, however, we need to develop and deploy algorithms which can exploit the structure of such hardware. The Large-scale Atomic/Molecular Massively Parallel Simulator (LAMMPS) is a scalable molecular dynamics code including long-range Coulomb interactions, which has been specifically designed to function efficiently on parallel platforms. Here we describe the implementation of the AMBER98 force field in LAMMPS and its validation for molecular dynamics investigations of DNA structure and flexibility against the benchmark of results obtained with the long-established code AMBER6 (Assisted Model Building with Energy Refinement, version 6). Extended molecular dynamics simulations on the hydrated DNA dodecamer d(CTTTTGCAAAAG)(2), which has previously been the subject of extensive dynamical analysis using AMBER6, show that it is possible to obtain excellent agreement in terms of static, dynamic and thermodynamic parameters between AMBER6 and LAMMPS. In comparison with AMBER6, LAMMPS shows greatly improved scalability in massively parallel environments, opening up the possibility of efficient simulations of order-of-magnitude larger systems and/or for order-of-magnitude greater simulation times.

A look at scalable dense linear algebra libraries

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dongarra, J.J.; Van de Geijn, R.A.; Walker, D.W.

1992-01-01

We discuss the essential design features of a library of scalable software for performing dense linear algebra computations on distributed memory concurrent computers. The square block scattered decomposition is proposed as a flexible and general-purpose way of decomposing most, if not all, dense matrix problems. An object- oriented interface to the library permits more portable applications to be written, and is easy to learn and use, since details of the parallel implementation are hidden from the user. Experiments on the Intel Touchstone Delta system with a prototype code that uses the square block scattered decomposition to perform LU factorization aremore » presented and analyzed. It was found that the code was both scalable and efficient, performing at about 14 GFLOPS (double precision) for the largest problem considered.« less

A look at scalable dense linear algebra libraries

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dongarra, J.J.; Van de Geijn, R.A.; Walker, D.W.

1992-08-01

We discuss the essential design features of a library of scalable software for performing dense linear algebra computations on distributed memory concurrent computers. The square block scattered decomposition is proposed as a flexible and general-purpose way of decomposing most, if not all, dense matrix problems. An object- oriented interface to the library permits more portable applications to be written, and is easy to learn and use, since details of the parallel implementation are hidden from the user. Experiments on the Intel Touchstone Delta system with a prototype code that uses the square block scattered decomposition to perform LU factorization aremore » presented and analyzed. It was found that the code was both scalable and efficient, performing at about 14 GFLOPS (double precision) for the largest problem considered.« less

Performance and scalability evaluation of "Big Memory" on Blue Gene Linux.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yoshii, K.; Iskra, K.; Naik, H.

2011-05-01

We address memory performance issues observed in Blue Gene Linux and discuss the design and implementation of 'Big Memory' - an alternative, transparent memory space introduced to eliminate the memory performance issues. We evaluate the performance of Big Memory using custom memory benchmarks, NAS Parallel Benchmarks, and the Parallel Ocean Program, at a scale of up to 4,096 nodes. We find that Big Memory successfully resolves the performance issues normally encountered in Blue Gene Linux. For the ocean simulation program, we even find that Linux with Big Memory provides better scalability than does the lightweight compute node kernel designed solelymore » for high-performance applications. Originally intended exclusively for compute node tasks, our new memory subsystem dramatically improves the performance of certain I/O node applications as well. We demonstrate this performance using the central processor of the LOw Frequency ARray radio telescope as an example.« less

Parallel peak pruning for scalable SMP contour tree computation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Carr, Hamish A.; Weber, Gunther H.; Sewell, Christopher M.

As data sets grow to exascale, automated data analysis and visualisation are increasingly important, to intermediate human understanding and to reduce demands on disk storage via in situ analysis. Trends in architecture of high performance computing systems necessitate analysis algorithms to make effective use of combinations of massively multicore and distributed systems. One of the principal analytic tools is the contour tree, which analyses relationships between contours to identify features of more than local importance. Unfortunately, the predominant algorithms for computing the contour tree are explicitly serial, and founded on serial metaphors, which has limited the scalability of this formmore » of analysis. While there is some work on distributed contour tree computation, and separately on hybrid GPU-CPU computation, there is no efficient algorithm with strong formal guarantees on performance allied with fast practical performance. Here in this paper, we report the first shared SMP algorithm for fully parallel contour tree computation, withfor-mal guarantees of O(lgnlgt) parallel steps and O(n lgn) work, and implementations with up to 10x parallel speed up in OpenMP and up to 50x speed up in NVIDIA Thrust.« less

Scalability study of parallel spatial direct numerical simulation code on IBM SP1 parallel supercomputer

NASA Technical Reports Server (NTRS)

Hanebutte, Ulf R.; Joslin, Ronald D.; Zubair, Mohammad

1994-01-01

The implementation and the performance of a parallel spatial direct numerical simulation (PSDNS) code are reported for the IBM SP1 supercomputer. The spatially evolving disturbances that are associated with laminar-to-turbulent in three-dimensional boundary-layer flows are computed with the PS-DNS code. By remapping the distributed data structure during the course of the calculation, optimized serial library routines can be utilized that substantially increase the computational performance. Although the remapping incurs a high communication penalty, the parallel efficiency of the code remains above 40% for all performed calculations. By using appropriate compile options and optimized library routines, the serial code achieves 52-56 Mflops on a single node of the SP1 (45% of theoretical peak performance). The actual performance of the PSDNS code on the SP1 is evaluated with a 'real world' simulation that consists of 1.7 million grid points. One time step of this simulation is calculated on eight nodes of the SP1 in the same time as required by a Cray Y/MP for the same simulation. The scalability information provides estimated computational costs that match the actual costs relative to changes in the number of grid points.

A Multi-Level Parallelization Concept for High-Fidelity Multi-Block Solvers

NASA Technical Reports Server (NTRS)

Hatay, Ferhat F.; Jespersen, Dennis C.; Guruswamy, Guru P.; Rizk, Yehia M.; Byun, Chansup; Gee, Ken; VanDalsem, William R. (Technical Monitor)

1997-01-01

The integration of high-fidelity Computational Fluid Dynamics (CFD) analysis tools with the industrial design process benefits greatly from the robust implementations that are transportable across a wide range of computer architectures. In the present work, a hybrid domain-decomposition and parallelization concept was developed and implemented into the widely-used NASA multi-block Computational Fluid Dynamics (CFD) packages implemented in ENSAERO and OVERFLOW. The new parallel solver concept, PENS (Parallel Euler Navier-Stokes Solver), employs both fine and coarse granularity in data partitioning as well as data coalescing to obtain the desired load-balance characteristics on the available computer platforms. This multi-level parallelism implementation itself introduces no changes to the numerical results, hence the original fidelity of the packages are identically preserved. The present implementation uses the Message Passing Interface (MPI) library for interprocessor message passing and memory accessing. By choosing an appropriate combination of the available partitioning and coalescing capabilities only during the execution stage, the PENS solver becomes adaptable to different computer architectures from shared-memory to distributed-memory platforms with varying degrees of parallelism. The PENS implementation on the IBM SP2 distributed memory environment at the NASA Ames Research Center obtains 85 percent scalable parallel performance using fine-grain partitioning of single-block CFD domains using up to 128 wide computational nodes. Multi-block CFD simulations of complete aircraft simulations achieve 75 percent perfect load-balanced executions using data coalescing and the two levels of parallelism. SGI PowerChallenge, SGI Origin 2000, and a cluster of workstations are the other platforms where the robustness of the implementation is tested. The performance behavior on the other computer platforms with a variety of realistic problems will be included as this on-going study progresses.

Applications Development for a Parallel COTS Spaceborne Computer

NASA Technical Reports Server (NTRS)

Katz, Daniel S.; Springer, Paul L.; Granat, Robert; Turmon, Michael

2000-01-01

This presentation reviews the Remote Exploration and Experimentation Project (REE) program for utilization of scalable supercomputing technology in space. The implementation of REE will be the use of COTS hardware and software to the maximum extent possible, keeping overhead low. Since COTS systems will be used, with little or no special modification, there will be significant cost reduction.

PIXIE3D: A Parallel, Implicit, eXtended MHD 3D Code.

NASA Astrophysics Data System (ADS)

Chacon, L.; Knoll, D. A.

2004-11-01

We report on the development of PIXIE3D, a 3D parallel, fully implicit Newton-Krylov extended primitive-variable MHD code in general curvilinear geometry. PIXIE3D employs a second-order, finite-volume-based spatial discretization that satisfies remarkable properties such as being conservative, solenoidal in the magnetic field, non-dissipative, and stable in the absence of physical dissipation.(L. Chacón , phComput. Phys. Comm.) submitted (2004) PIXIE3D employs fully-implicit Newton-Krylov methods for the time advance. Currently, first and second-order implicit schemes are available, although higher-order temporal implicit schemes can be effortlessly implemented within the Newton-Krylov framework. A successful, scalable, MG physics-based preconditioning strategy, similar in concept to previous 2D MHD efforts,(L. Chacón et al., phJ. Comput. Phys). 178 (1), 15- 36 (2002); phJ. Comput. Phys., 188 (2), 573-592 (2003) has been developed. We are currently in the process of parallelizing the code using the PETSc library, and a Newton-Krylov-Schwarz approach for the parallel treatment of the preconditioner. In this poster, we will report on both the serial and parallel performance of PIXIE3D, focusing primarily on scalability and CPU speedup vs. an explicit approach.

Efficient Scalable Median Filtering Using Histogram-Based Operations.

PubMed

Green, Oded

2018-05-01

Median filtering is a smoothing technique for noise removal in images. While there are various implementations of median filtering for a single-core CPU, there are few implementations for accelerators and multi-core systems. Many parallel implementations of median filtering use a sorting algorithm for rearranging the values within a filtering window and taking the median of the sorted value. While using sorting algorithms allows for simple parallel implementations, the cost of the sorting becomes prohibitive as the filtering windows grow. This makes such algorithms, sequential and parallel alike, inefficient. In this work, we introduce the first software parallel median filtering that is non-sorting-based. The new algorithm uses efficient histogram-based operations. These reduce the computational requirements of the new algorithm while also accessing the image fewer times. We show an implementation of our algorithm for both the CPU and NVIDIA's CUDA supported graphics processing unit (GPU). The new algorithm is compared with several other leading CPU and GPU implementations. The CPU implementation has near perfect linear scaling with a speedup on a quad-core system. The GPU implementation is several orders of magnitude faster than the other GPU implementations for mid-size median filters. For small kernels, and , comparison-based approaches are preferable as fewer operations are required. Lastly, the new algorithm is open-source and can be found in the OpenCV library.

A high performance linear equation solver on the VPP500 parallel supercomputer

DOE Office of Scientific and Technical Information (OSTI.GOV)

Nakanishi, Makoto; Ina, Hiroshi; Miura, Kenichi

1994-12-31

This paper describes the implementation of two high performance linear equation solvers developed for the Fujitsu VPP500, a distributed memory parallel supercomputer system. The solvers take advantage of the key architectural features of VPP500--(1) scalability for an arbitrary number of processors up to 222 processors, (2) flexible data transfer among processors provided by a crossbar interconnection network, (3) vector processing capability on each processor, and (4) overlapped computation and transfer. The general linear equation solver based on the blocked LU decomposition method achieves 120.0 GFLOPS performance with 100 processors in the LIN-PACK Highly Parallel Computing benchmark.

Fast l₁-SPIRiT compressed sensing parallel imaging MRI: scalable parallel implementation and clinically feasible runtime.

PubMed

Murphy, Mark; Alley, Marcus; Demmel, James; Keutzer, Kurt; Vasanawala, Shreyas; Lustig, Michael

2012-06-01

We present l₁-SPIRiT, a simple algorithm for auto calibrating parallel imaging (acPI) and compressed sensing (CS) that permits an efficient implementation with clinically-feasible runtimes. We propose a CS objective function that minimizes cross-channel joint sparsity in the wavelet domain. Our reconstruction minimizes this objective via iterative soft-thresholding, and integrates naturally with iterative self-consistent parallel imaging (SPIRiT). Like many iterative magnetic resonance imaging reconstructions, l₁-SPIRiT's image quality comes at a high computational cost. Excessively long runtimes are a barrier to the clinical use of any reconstruction approach, and thus we discuss our approach to efficiently parallelizing l₁-SPIRiT and to achieving clinically-feasible runtimes. We present parallelizations of l₁-SPIRiT for both multi-GPU systems and multi-core CPUs, and discuss the software optimization and parallelization decisions made in our implementation. The performance of these alternatives depends on the processor architecture, the size of the image matrix, and the number of parallel imaging channels. Fundamentally, achieving fast runtime requires the correct trade-off between cache usage and parallelization overheads. We demonstrate image quality via a case from our clinical experimentation, using a custom 3DFT spoiled gradient echo (SPGR) sequence with up to 8× acceleration via Poisson-disc undersampling in the two phase-encoded directions.

Fast ℓ1-SPIRiT Compressed Sensing Parallel Imaging MRI: Scalable Parallel Implementation and Clinically Feasible Runtime

PubMed Central

Murphy, Mark; Alley, Marcus; Demmel, James; Keutzer, Kurt; Vasanawala, Shreyas; Lustig, Michael

2012-01-01

We present ℓ1-SPIRiT, a simple algorithm for auto calibrating parallel imaging (acPI) and compressed sensing (CS) that permits an efficient implementation with clinically-feasible runtimes. We propose a CS objective function that minimizes cross-channel joint sparsity in the Wavelet domain. Our reconstruction minimizes this objective via iterative soft-thresholding, and integrates naturally with iterative Self-Consistent Parallel Imaging (SPIRiT). Like many iterative MRI reconstructions, ℓ1-SPIRiT’s image quality comes at a high computational cost. Excessively long runtimes are a barrier to the clinical use of any reconstruction approach, and thus we discuss our approach to efficiently parallelizing ℓ1-SPIRiT and to achieving clinically-feasible runtimes. We present parallelizations of ℓ1-SPIRiT for both multi-GPU systems and multi-core CPUs, and discuss the software optimization and parallelization decisions made in our implementation. The performance of these alternatives depends on the processor architecture, the size of the image matrix, and the number of parallel imaging channels. Fundamentally, achieving fast runtime requires the correct trade-off between cache usage and parallelization overheads. We demonstrate image quality via a case from our clinical experimentation, using a custom 3DFT Spoiled Gradient Echo (SPGR) sequence with up to 8× acceleration via poisson-disc undersampling in the two phase-encoded directions. PMID:22345529

Parallel Climate Data Assimilation PSAS Package

NASA Technical Reports Server (NTRS)

Ding, Hong Q.; Chan, Clara; Gennery, Donald B.; Ferraro, Robert D.

1996-01-01

We have designed and implemented a set of highly efficient and highly scalable algorithms for an unstructured computational package, the PSAS data assimilation package, as demonstrated by detailed performance analysis of systematic runs on up to 512node Intel Paragon. The equation solver achieves a sustained 18 Gflops performance. As the results, we achieved an unprecedented 100-fold solution time reduction on the Intel Paragon parallel platform over the Cray C90. This not only meets and exceeds the DAO time requirements, but also significantly enlarges the window of exploration in climate data assimilations.

Parallel and fault-tolerant algorithms for hypercube multiprocessors

DOE Office of Scientific and Technical Information (OSTI.GOV)

Aykanat, C.

1988-01-01

Several techniques for increasing the performance of parallel algorithms on distributed-memory message-passing multi-processor systems are investigated. These techniques are effectively implemented for the parallelization of the Scaled Conjugate Gradient (SCG) algorithm on a hypercube connected message-passing multi-processor. Significant performance improvement is achieved by using these techniques. The SCG algorithm is used for the solution phase of an FE modeling system. Almost linear speed-up is achieved, and it is shown that hypercube topology is scalable for an FE class of problem. The SCG algorithm is also shown to be suitable for vectorization, and near supercomputer performance is achieved on a vectormore » hypercube multiprocessor by exploiting both parallelization and vectorization. Fault-tolerance issues for the parallel SCG algorithm and for the hypercube topology are also addressed.« less

Scalable nuclear density functional theory with Sky3D

NASA Astrophysics Data System (ADS)

Afibuzzaman, Md; Schuetrumpf, Bastian; Aktulga, Hasan Metin

2018-02-01

In nuclear astrophysics, quantum simulations of large inhomogeneous dense systems as they appear in the crusts of neutron stars present big challenges. The number of particles in a simulation with periodic boundary conditions is strongly limited due to the immense computational cost of the quantum methods. In this paper, we describe techniques for an efficient and scalable parallel implementation of Sky3D, a nuclear density functional theory solver that operates on an equidistant grid. Presented techniques allow Sky3D to achieve good scaling and high performance on a large number of cores, as demonstrated through detailed performance analysis on a Cray XC40 supercomputer.

National Combustion Code Parallel Performance Enhancements

NASA Technical Reports Server (NTRS)

Quealy, Angela; Benyo, Theresa (Technical Monitor)

2002-01-01

The National Combustion Code (NCC) is being developed by an industry-government team for the design and analysis of combustion systems. The unstructured grid, reacting flow code uses a distributed memory, message passing model for its parallel implementation. The focus of the present effort has been to improve the performance of the NCC code to meet combustor designer requirements for model accuracy and analysis turnaround time. Improving the performance of this code contributes significantly to the overall reduction in time and cost of the combustor design cycle. This report describes recent parallel processing modifications to NCC that have improved the parallel scalability of the code, enabling a two hour turnaround for a 1.3 million element fully reacting combustion simulation on an SGI Origin 2000.

Scalable isosurface visualization of massive datasets on commodity off-the-shelf clusters

PubMed Central

Bajaj, Chandrajit

2009-01-01

Tomographic imaging and computer simulations are increasingly yielding massive datasets. Interactive and exploratory visualizations have rapidly become indispensable tools to study large volumetric imaging and simulation data. Our scalable isosurface visualization framework on commodity off-the-shelf clusters is an end-to-end parallel and progressive platform, from initial data access to the final display. Interactive browsing of extracted isosurfaces is made possible by using parallel isosurface extraction, and rendering in conjunction with a new specialized piece of image compositing hardware called Metabuffer. In this paper, we focus on the back end scalability by introducing a fully parallel and out-of-core isosurface extraction algorithm. It achieves scalability by using both parallel and out-of-core processing and parallel disks. It statically partitions the volume data to parallel disks with a balanced workload spectrum, and builds I/O-optimal external interval trees to minimize the number of I/O operations of loading large data from disk. We also describe an isosurface compression scheme that is efficient for progress extraction, transmission and storage of isosurfaces. PMID:19756231

The ELPA library: scalable parallel eigenvalue solutions for electronic structure theory and computational science.

PubMed

Marek, A; Blum, V; Johanni, R; Havu, V; Lang, B; Auckenthaler, T; Heinecke, A; Bungartz, H-J; Lederer, H

2014-05-28

Obtaining the eigenvalues and eigenvectors of large matrices is a key problem in electronic structure theory and many other areas of computational science. The computational effort formally scales as O(N(3)) with the size of the investigated problem, N (e.g. the electron count in electronic structure theory), and thus often defines the system size limit that practical calculations cannot overcome. In many cases, more than just a small fraction of the possible eigenvalue/eigenvector pairs is needed, so that iterative solution strategies that focus only on a few eigenvalues become ineffective. Likewise, it is not always desirable or practical to circumvent the eigenvalue solution entirely. We here review some current developments regarding dense eigenvalue solvers and then focus on the Eigenvalue soLvers for Petascale Applications (ELPA) library, which facilitates the efficient algebraic solution of symmetric and Hermitian eigenvalue problems for dense matrices that have real-valued and complex-valued matrix entries, respectively, on parallel computer platforms. ELPA addresses standard as well as generalized eigenvalue problems, relying on the well documented matrix layout of the Scalable Linear Algebra PACKage (ScaLAPACK) library but replacing all actual parallel solution steps with subroutines of its own. For these steps, ELPA significantly outperforms the corresponding ScaLAPACK routines and proprietary libraries that implement the ScaLAPACK interface (e.g. Intel's MKL). The most time-critical step is the reduction of the matrix to tridiagonal form and the corresponding backtransformation of the eigenvectors. ELPA offers both a one-step tridiagonalization (successive Householder transformations) and a two-step transformation that is more efficient especially towards larger matrices and larger numbers of CPU cores. ELPA is based on the MPI standard, with an early hybrid MPI-OpenMPI implementation available as well. Scalability beyond 10,000 CPU cores for problem sizes arising in the field of electronic structure theory is demonstrated for current high-performance computer architectures such as Cray or Intel/Infiniband. For a matrix of dimension 260,000, scalability up to 295,000 CPU cores has been shown on BlueGene/P.

GPU-accelerated Tersoff potentials for massively parallel Molecular Dynamics simulations

NASA Astrophysics Data System (ADS)

Nguyen, Trung Dac

2017-03-01

The Tersoff potential is one of the empirical many-body potentials that has been widely used in simulation studies at atomic scales. Unlike pair-wise potentials, the Tersoff potential involves three-body terms, which require much more arithmetic operations and data dependency. In this contribution, we have implemented the GPU-accelerated version of several variants of the Tersoff potential for LAMMPS, an open-source massively parallel Molecular Dynamics code. Compared to the existing MPI implementation in LAMMPS, the GPU implementation exhibits a better scalability and offers a speedup of 2.2X when run on 1000 compute nodes on the Titan supercomputer. On a single node, the speedup ranges from 2.0 to 8.0 times, depending on the number of atoms per GPU and hardware configurations. The most notable features of our GPU-accelerated version include its design for MPI/accelerator heterogeneous parallelism, its compatibility with other functionalities in LAMMPS, its ability to give deterministic results and to support both NVIDIA CUDA- and OpenCL-enabled accelerators. Our implementation is now part of the GPU package in LAMMPS and accessible for public use.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Janjusic, Tommy; Kartsaklis, Christos

Memory scalability is an enduring problem and bottleneck that plagues many parallel codes. Parallel codes designed for High Performance Systems are typically designed over the span of several, and in some instances 10+, years. As a result, optimization practices which were appropriate for earlier systems may no longer be valid and thus require careful optimization consideration. Specifically, parallel codes whose memory footprint is a function of their scalability must be carefully considered for future exa-scale systems. In this paper we present a methodology and tool to study the memory scalability of parallel codes. Using our methodology we evaluate an applicationmore » s memory footprint as a function of scalability, which we coined memory efficiency, and describe our results. In particular, using our in-house tools we can pinpoint the specific application components which contribute to the application s overall memory foot-print (application data- structures, libraries, etc.).« less

Implementation, capabilities, and benchmarking of Shift, a massively parallel Monte Carlo radiation transport code

DOE PAGES

Pandya, Tara M.; Johnson, Seth R.; Evans, Thomas M.; ...

2015-12-21

This paper discusses the implementation, capabilities, and validation of Shift, a massively parallel Monte Carlo radiation transport package developed and maintained at Oak Ridge National Laboratory. It has been developed to scale well from laptop to small computing clusters to advanced supercomputers. Special features of Shift include hybrid capabilities for variance reduction such as CADIS and FW-CADIS, and advanced parallel decomposition and tally methods optimized for scalability on supercomputing architectures. Shift has been validated and verified against various reactor physics benchmarks and compares well to other state-of-the-art Monte Carlo radiation transport codes such as MCNP5, CE KENO-VI, and OpenMC. Somemore » specific benchmarks used for verification and validation include the CASL VERA criticality test suite and several Westinghouse AP1000 ® problems. These benchmark and scaling studies show promising results.« less

Parallel k-means++ for Multiple Shared-Memory Architectures

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mackey, Patrick S.; Lewis, Robert R.

2016-09-22

In recent years k-means++ has become a popular initialization technique for improved k-means clustering. To date, most of the work done to improve its performance has involved parallelizing algorithms that are only approximations of k-means++. In this paper we present a parallelization of the exact k-means++ algorithm, with a proof of its correctness. We develop implementations for three distinct shared-memory architectures: multicore CPU, high performance GPU, and the massively multithreaded Cray XMT platform. We demonstrate the scalability of the algorithm on each platform. In addition we present a visual approach for showing which platform performed k-means++ the fastest for varyingmore » data sizes.« less

Construction and comparison of parallel implicit kinetic solvers in three spatial dimensions

NASA Astrophysics Data System (ADS)

Titarev, Vladimir; Dumbser, Michael; Utyuzhnikov, Sergey

2014-01-01

The paper is devoted to the further development and systematic performance evaluation of a recent deterministic framework Nesvetay-3D for modelling three-dimensional rarefied gas flows. Firstly, a review of the existing discretization and parallelization strategies for solving numerically the Boltzmann kinetic equation with various model collision integrals is carried out. Secondly, a new parallelization strategy for the implicit time evolution method is implemented which improves scaling on large CPU clusters. Accuracy and scalability of the methods are demonstrated on a pressure-driven rarefied gas flow through a finite-length circular pipe as well as an external supersonic flow over a three-dimensional re-entry geometry of complicated aerodynamic shape.

Scalable Domain Decomposed Monte Carlo Particle Transport

DOE Office of Scientific and Technical Information (OSTI.GOV)

O'Brien, Matthew Joseph

2013-12-05

In this dissertation, we present the parallel algorithms necessary to run domain decomposed Monte Carlo particle transport on large numbers of processors (millions of processors). Previous algorithms were not scalable, and the parallel overhead became more computationally costly than the numerical simulation.

Joint Experimentation on Scalable Parallel Processors (JESPP)

DTIC Science & Technology

2006-04-01

made use of local embedded relational databases, implemented using sqlite on each node of an SPP to execute queries and return results via an ad hoc ...rl.af.mil 12a. DISTRIBUTION / AVAILABILITY STATEENT APPROVED FOR PUBLIC RELEASE; DISTRIBUTION UNLIMITED. 12b. DISTRIBUTION CODE 13. ABSTRACT...Experimentation Directorate (J9) required expansion of its joint semi-automated forces (JSAF) code capabilities; including number of entities, behavior complexity

Reversible Parallel Discrete-Event Execution of Large-scale Epidemic Outbreak Models

DOE Office of Scientific and Technical Information (OSTI.GOV)

Perumalla, Kalyan S; Seal, Sudip K

2010-01-01

The spatial scale, runtime speed and behavioral detail of epidemic outbreak simulations together require the use of large-scale parallel processing. In this paper, an optimistic parallel discrete event execution of a reaction-diffusion simulation model of epidemic outbreaks is presented, with an implementation over themore » $$\\mu$$sik simulator. Rollback support is achieved with the development of a novel reversible model that combines reverse computation with a small amount of incremental state saving. Parallel speedup and other runtime performance metrics of the simulation are tested on a small (8,192-core) Blue Gene / P system, while scalability is demonstrated on 65,536 cores of a large Cray XT5 system. Scenarios representing large population sizes (up to several hundred million individuals in the largest case) are exercised.« less

A Fast parallel tridiagonal algorithm for a class of CFD applications

NASA Technical Reports Server (NTRS)

Moitra, Stuti; Sun, Xian-He

1996-01-01

The parallel diagonal dominant (PDD) algorithm is an efficient tridiagonal solver. This paper presents for study a variation of the PDD algorithm, the reduced PDD algorithm. The new algorithm maintains the minimum communication provided by the PDD algorithm, but has a reduced operation count. The PDD algorithm also has a smaller operation count than the conventional sequential algorithm for many applications. Accuracy analysis is provided for the reduced PDD algorithm for symmetric Toeplitz tridiagonal (STT) systems. Implementation results on Langley's Intel Paragon and IBM SP2 show that both the PDD and reduced PDD algorithms are efficient and scalable.

Proteus-MOC: A 3D deterministic solver incorporating 2D method of characteristics

DOE Office of Scientific and Technical Information (OSTI.GOV)

Marin-Lafleche, A.; Smith, M. A.; Lee, C.

2013-07-01

A new transport solution methodology was developed by combining the two-dimensional method of characteristics with the discontinuous Galerkin method for the treatment of the axial variable. The method, which can be applied to arbitrary extruded geometries, was implemented in PROTEUS-MOC and includes parallelization in group, angle, plane, and space using a top level GMRES linear algebra solver. Verification tests were performed to show accuracy and stability of the method with the increased number of angular directions and mesh elements. Good scalability with parallelism in angle and axial planes is displayed. (authors)

The GBS code for tokamak scrape-off layer simulations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Halpern, F.D., E-mail: federico.halpern@epfl.ch; Ricci, P.; Jolliet, S.

2016-06-15

We describe a new version of GBS, a 3D global, flux-driven plasma turbulence code to simulate the turbulent dynamics in the tokamak scrape-off layer (SOL), superseding the code presented by Ricci et al. (2012) [14]. The present work is driven by the objective of studying SOL turbulent dynamics in medium size tokamaks and beyond with a high-fidelity physics model. We emphasize an intertwining framework of improved physics models and the computational improvements that allow them. The model extensions include neutral atom physics, finite ion temperature, the addition of a closed field line region, and a non-Boussinesq treatment of the polarizationmore » drift. GBS has been completely refactored with the introduction of a 3-D Cartesian communicator and a scalable parallel multigrid solver. We report dramatically enhanced parallel scalability, with the possibility of treating electromagnetic fluctuations very efficiently. The method of manufactured solutions as a verification process has been carried out for this new code version, demonstrating the correct implementation of the physical model.« less

Voxel based parallel post processor for void nucleation and growth analysis of atomistic simulations of material fracture.

PubMed

Hemani, H; Warrier, M; Sakthivel, N; Chaturvedi, S

2014-05-01

Molecular dynamics (MD) simulations are used in the study of void nucleation and growth in crystals that are subjected to tensile deformation. These simulations are run for typically several hundred thousand time steps depending on the problem. We output the atom positions at a required frequency for post processing to determine the void nucleation, growth and coalescence due to tensile deformation. The simulation volume is broken up into voxels of size equal to the unit cell size of crystal. In this paper, we present the algorithm to identify the empty unit cells (voids), their connections (void size) and dynamic changes (growth and coalescence of voids) for MD simulations of large atomic systems (multi-million atoms). We discuss the parallel algorithms that were implemented and discuss their relative applicability in terms of their speedup and scalability. We also present the results on scalability of our algorithm when it is incorporated into MD software LAMMPS. Copyright © 2014 Elsevier Inc. All rights reserved.

Implementation of a Parallel Kalman Filter for Stratospheric Chemical Tracer Assimilation

NASA Technical Reports Server (NTRS)

Chang, Lang-Ping; Lyster, Peter M.; Menard, R.; Cohn, S. E.

1998-01-01

A Kalman filter for the assimilation of long-lived atmospheric chemical constituents has been developed for two-dimensional transport models on isentropic surfaces over the globe. An important attribute of the Kalman filter is that it calculates error covariances of the constituent fields using the tracer dynamics. Consequently, the current Kalman-filter assimilation is a five-dimensional problem (coordinates of two points and time), and it can only be handled on computers with large memory and high floating point speed. In this paper, an implementation of the Kalman filter for distributed-memory, message-passing parallel computers is discussed. Two approaches were studied: an operator decomposition and a covariance decomposition. The latter was found to be more scalable than the former, and it possesses the property that the dynamical model does not need to be parallelized, which is of considerable practical advantage. This code is currently used to assimilate constituent data retrieved by limb sounders on the Upper Atmosphere Research Satellite. Tests of the code examined the variance transport and observability properties. Aspects of the parallel implementation, some timing results, and a brief discussion of the physical results will be presented.

Analysis of scalability of high-performance 3D image processing platform for virtual colonoscopy

NASA Astrophysics Data System (ADS)

Yoshida, Hiroyuki; Wu, Yin; Cai, Wenli

2014-03-01

One of the key challenges in three-dimensional (3D) medical imaging is to enable the fast turn-around time, which is often required for interactive or real-time response. This inevitably requires not only high computational power but also high memory bandwidth due to the massive amount of data that need to be processed. For this purpose, we previously developed a software platform for high-performance 3D medical image processing, called HPC 3D-MIP platform, which employs increasingly available and affordable commodity computing systems such as the multicore, cluster, and cloud computing systems. To achieve scalable high-performance computing, the platform employed size-adaptive, distributable block volumes as a core data structure for efficient parallelization of a wide range of 3D-MIP algorithms, supported task scheduling for efficient load distribution and balancing, and consisted of a layered parallel software libraries that allow image processing applications to share the common functionalities. We evaluated the performance of the HPC 3D-MIP platform by applying it to computationally intensive processes in virtual colonoscopy. Experimental results showed a 12-fold performance improvement on a workstation with 12-core CPUs over the original sequential implementation of the processes, indicating the efficiency of the platform. Analysis of performance scalability based on the Amdahl's law for symmetric multicore chips showed the potential of a high performance scalability of the HPC 3DMIP platform when a larger number of cores is available.

A Systems Approach to Scalable Transportation Network Modeling

DOE Office of Scientific and Technical Information (OSTI.GOV)

Perumalla, Kalyan S

2006-01-01

Emerging needs in transportation network modeling and simulation are raising new challenges with respect to scal-ability of network size and vehicular traffic intensity, speed of simulation for simulation-based optimization, and fidel-ity of vehicular behavior for accurate capture of event phe-nomena. Parallel execution is warranted to sustain the re-quired detail, size and speed. However, few parallel simulators exist for such applications, partly due to the challenges underlying their development. Moreover, many simulators are based on time-stepped models, which can be computationally inefficient for the purposes of modeling evacuation traffic. Here an approach is presented to de-signing a simulator with memory andmore » speed efficiency as the goals from the outset, and, specifically, scalability via parallel execution. The design makes use of discrete event modeling techniques as well as parallel simulation meth-ods. Our simulator, called SCATTER, is being developed, incorporating such design considerations. Preliminary per-formance results are presented on benchmark road net-works, showing scalability to one million vehicles simu-lated on one processor.« less

Parallel algorithms for large-scale biological sequence alignment on Xeon-Phi based clusters.

PubMed

Lan, Haidong; Chan, Yuandong; Xu, Kai; Schmidt, Bertil; Peng, Shaoliang; Liu, Weiguo

2016-07-19

Computing alignments between two or more sequences are common operations frequently performed in computational molecular biology. The continuing growth of biological sequence databases establishes the need for their efficient parallel implementation on modern accelerators. This paper presents new approaches to high performance biological sequence database scanning with the Smith-Waterman algorithm and the first stage of progressive multiple sequence alignment based on the ClustalW heuristic on a Xeon Phi-based compute cluster. Our approach uses a three-level parallelization scheme to take full advantage of the compute power available on this type of architecture; i.e. cluster-level data parallelism, thread-level coarse-grained parallelism, and vector-level fine-grained parallelism. Furthermore, we re-organize the sequence datasets and use Xeon Phi shuffle operations to improve I/O efficiency. Evaluations show that our method achieves a peak overall performance up to 220 GCUPS for scanning real protein sequence databanks on a single node consisting of two Intel E5-2620 CPUs and two Intel Xeon Phi 7110P cards. It also exhibits good scalability in terms of sequence length and size, and number of compute nodes for both database scanning and multiple sequence alignment. Furthermore, the achieved performance is highly competitive in comparison to optimized Xeon Phi and GPU implementations. Our implementation is available at https://github.com/turbo0628/LSDBS-mpi .

Scalable and massively parallel Monte Carlo photon transport simulations for heterogeneous computing platforms

NASA Astrophysics Data System (ADS)

Yu, Leiming; Nina-Paravecino, Fanny; Kaeli, David; Fang, Qianqian

2018-01-01

We present a highly scalable Monte Carlo (MC) three-dimensional photon transport simulation platform designed for heterogeneous computing systems. Through the development of a massively parallel MC algorithm using the Open Computing Language framework, this research extends our existing graphics processing unit (GPU)-accelerated MC technique to a highly scalable vendor-independent heterogeneous computing environment, achieving significantly improved performance and software portability. A number of parallel computing techniques are investigated to achieve portable performance over a wide range of computing hardware. Furthermore, multiple thread-level and device-level load-balancing strategies are developed to obtain efficient simulations using multiple central processing units and GPUs.

Impact of the implementation of MPI point-to-point communications on the performance of two general sparse solvers

DOE Office of Scientific and Technical Information (OSTI.GOV)

Amestoy, Patrick R.; Duff, Iain S.; L'Excellent, Jean-Yves

2001-10-10

We examine the mechanics of the send and receive mechanism of MPI and in particular how we can implement message passing in a robust way so that our performance is not significantly affected by changes to the MPI system. This leads us to using the Isend/Irecv protocol which will entail sometimes significant algorithmic changes. We discuss this within the context of two different algorithms for sparse Gaussian elimination that we have parallelized. One is a multifrontal solver called MUMPS, the other is a supernodal solver called SuperLU. Both algorithms are difficult to parallelize on distributed memory machines. Our initial strategiesmore » were based on simple MPI point-to-point communication primitives. With such approaches, the parallel performance of both codes are very sensitive to the MPI implementation, the way MPI internal buffers are used in particular. We then modified our codes to use more sophisticated nonblocking versions of MPI communication. This significantly improved the performance robustness (independent of the MPI buffering mechanism) and scalability, but at the cost of increased code complexity.« less

Cloud Computing Boosts Business Intelligence of Telecommunication Industry

NASA Astrophysics Data System (ADS)

Xu, Meng; Gao, Dan; Deng, Chao; Luo, Zhiguo; Sun, Shaoling

Business Intelligence becomes an attracting topic in today's data intensive applications, especially in telecommunication industry. Meanwhile, Cloud Computing providing IT supporting Infrastructure with excellent scalability, large scale storage, and high performance becomes an effective way to implement parallel data processing and data mining algorithms. BC-PDM (Big Cloud based Parallel Data Miner) is a new MapReduce based parallel data mining platform developed by CMRI (China Mobile Research Institute) to fit the urgent requirements of business intelligence in telecommunication industry. In this paper, the architecture, functionality and performance of BC-PDM are presented, together with the experimental evaluation and case studies of its applications. The evaluation result demonstrates both the usability and the cost-effectiveness of Cloud Computing based Business Intelligence system in applications of telecommunication industry.

SBML-PET-MPI: a parallel parameter estimation tool for Systems Biology Markup Language based models.

PubMed

Zi, Zhike

2011-04-01

Parameter estimation is crucial for the modeling and dynamic analysis of biological systems. However, implementing parameter estimation is time consuming and computationally demanding. Here, we introduced a parallel parameter estimation tool for Systems Biology Markup Language (SBML)-based models (SBML-PET-MPI). SBML-PET-MPI allows the user to perform parameter estimation and parameter uncertainty analysis by collectively fitting multiple experimental datasets. The tool is developed and parallelized using the message passing interface (MPI) protocol, which provides good scalability with the number of processors. SBML-PET-MPI is freely available for non-commercial use at http://www.bioss.uni-freiburg.de/cms/sbml-pet-mpi.html or http://sites.google.com/site/sbmlpetmpi/.

Shift-and-invert parallel spectral transformation eigensolver: Massively parallel performance for density-functional based tight-binding

DOE PAGES

Zhang, Hong; Zapol, Peter; Dixon, David A.; ...

2015-11-17

The Shift-and-invert parallel spectral transformations (SIPs), a computational approach to solve sparse eigenvalue problems, is developed for massively parallel architectures with exceptional parallel scalability and robustness. The capabilities of SIPs are demonstrated by diagonalization of density-functional based tight-binding (DFTB) Hamiltonian and overlap matrices for single-wall metallic carbon nanotubes, diamond nanowires, and bulk diamond crystals. The largest (smallest) example studied is a 128,000 (2000) atom nanotube for which ~330,000 (~5600) eigenvalues and eigenfunctions are obtained in ~190 (~5) seconds when parallelized over 266,144 (16,384) Blue Gene/Q cores. Weak scaling and strong scaling of SIPs are analyzed and the performance of SIPsmore » is compared with other novel methods. Different matrix ordering methods are investigated to reduce the cost of the factorization step, which dominates the time-to-solution at the strong scaling limit. As a result, a parallel implementation of assembling the density matrix from the distributed eigenvectors is demonstrated.« less

Shift-and-invert parallel spectral transformation eigensolver: Massively parallel performance for density-functional based tight-binding

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhang, Hong; Zapol, Peter; Dixon, David A.

The Shift-and-invert parallel spectral transformations (SIPs), a computational approach to solve sparse eigenvalue problems, is developed for massively parallel architectures with exceptional parallel scalability and robustness. The capabilities of SIPs are demonstrated by diagonalization of density-functional based tight-binding (DFTB) Hamiltonian and overlap matrices for single-wall metallic carbon nanotubes, diamond nanowires, and bulk diamond crystals. The largest (smallest) example studied is a 128,000 (2000) atom nanotube for which ~330,000 (~5600) eigenvalues and eigenfunctions are obtained in ~190 (~5) seconds when parallelized over 266,144 (16,384) Blue Gene/Q cores. Weak scaling and strong scaling of SIPs are analyzed and the performance of SIPsmore » is compared with other novel methods. Different matrix ordering methods are investigated to reduce the cost of the factorization step, which dominates the time-to-solution at the strong scaling limit. As a result, a parallel implementation of assembling the density matrix from the distributed eigenvectors is demonstrated.« less

S-HARP: A parallel dynamic spectral partitioner

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sohn, A.; Simon, H.

1998-01-01

Computational science problems with adaptive meshes involve dynamic load balancing when implemented on parallel machines. This dynamic load balancing requires fast partitioning of computational meshes at run time. The authors present in this report a fast parallel dynamic partitioner, called S-HARP. The underlying principles of S-HARP are the fast feature of inertial partitioning and the quality feature of spectral partitioning. S-HARP partitions a graph from scratch, requiring no partition information from previous iterations. Two types of parallelism have been exploited in S-HARP, fine grain loop level parallelism and coarse grain recursive parallelism. The parallel partitioner has been implemented in Messagemore » Passing Interface on Cray T3E and IBM SP2 for portability. Experimental results indicate that S-HARP can partition a mesh of over 100,000 vertices into 256 partitions in 0.2 seconds on a 64 processor Cray T3E. S-HARP is much more scalable than other dynamic partitioners, giving over 15 fold speedup on 64 processors while ParaMeTiS1.0 gives a few fold speedup. Experimental results demonstrate that S-HARP is three to 10 times faster than the dynamic partitioners ParaMeTiS and Jostle on six computational meshes of size over 100,000 vertices.« less

Computing Maximum Cardinality Matchings in Parallel on Bipartite Graphs via Tree-Grafting

DOE Office of Scientific and Technical Information (OSTI.GOV)

Azad, Ariful; Buluc, Aydn; Pothen, Alex

It is difficult to obtain high performance when computing matchings on parallel processors because matching algorithms explicitly or implicitly search for paths in the graph, and when these paths become long, there is little concurrency. In spite of this limitation, we present a new algorithm and its shared-memory parallelization that achieves good performance and scalability in computing maximum cardinality matchings in bipartite graphs. This algorithm searches for augmenting paths via specialized breadth-first searches (BFS) from multiple source vertices, hence creating more parallelism than single source algorithms. Algorithms that employ multiple-source searches cannot discard a search tree once no augmenting pathmore » is discovered from the tree, unlike algorithms that rely on single-source searches. We describe a novel tree-grafting method that eliminates most of the redundant edge traversals resulting from this property of multiple-source searches. We also employ the recent direction-optimizing BFS algorithm as a subroutine to discover augmenting paths faster. Our algorithm compares favorably with the current best algorithms in terms of the number of edges traversed, the average augmenting path length, and the number of iterations. Here, we provide a proof of correctness for our algorithm. Our NUMA-aware implementation is scalable to 80 threads of an Intel multiprocessor and to 240 threads on an Intel Knights Corner coprocessor. On average, our parallel algorithm runs an order of magnitude faster than the fastest algorithms available. The performance improvement is more significant on graphs with small matching number.« less

Computing Maximum Cardinality Matchings in Parallel on Bipartite Graphs via Tree-Grafting

DOE PAGES

Azad, Ariful; Buluc, Aydn; Pothen, Alex

2016-03-24

It is difficult to obtain high performance when computing matchings on parallel processors because matching algorithms explicitly or implicitly search for paths in the graph, and when these paths become long, there is little concurrency. In spite of this limitation, we present a new algorithm and its shared-memory parallelization that achieves good performance and scalability in computing maximum cardinality matchings in bipartite graphs. This algorithm searches for augmenting paths via specialized breadth-first searches (BFS) from multiple source vertices, hence creating more parallelism than single source algorithms. Algorithms that employ multiple-source searches cannot discard a search tree once no augmenting pathmore » is discovered from the tree, unlike algorithms that rely on single-source searches. We describe a novel tree-grafting method that eliminates most of the redundant edge traversals resulting from this property of multiple-source searches. We also employ the recent direction-optimizing BFS algorithm as a subroutine to discover augmenting paths faster. Our algorithm compares favorably with the current best algorithms in terms of the number of edges traversed, the average augmenting path length, and the number of iterations. Here, we provide a proof of correctness for our algorithm. Our NUMA-aware implementation is scalable to 80 threads of an Intel multiprocessor and to 240 threads on an Intel Knights Corner coprocessor. On average, our parallel algorithm runs an order of magnitude faster than the fastest algorithms available. The performance improvement is more significant on graphs with small matching number.« less

Real-time SHVC software decoding with multi-threaded parallel processing

NASA Astrophysics Data System (ADS)

Gudumasu, Srinivas; He, Yuwen; Ye, Yan; He, Yong; Ryu, Eun-Seok; Dong, Jie; Xiu, Xiaoyu

2014-09-01

This paper proposes a parallel decoding framework for scalable HEVC (SHVC). Various optimization technologies are implemented on the basis of SHVC reference software SHM-2.0 to achieve real-time decoding speed for the two layer spatial scalability configuration. SHVC decoder complexity is analyzed with profiling information. The decoding process at each layer and the up-sampling process are designed in parallel and scheduled by a high level application task manager. Within each layer, multi-threaded decoding is applied to accelerate the layer decoding speed. Entropy decoding, reconstruction, and in-loop processing are pipeline designed with multiple threads based on groups of coding tree units (CTU). A group of CTUs is treated as a processing unit in each pipeline stage to achieve a better trade-off between parallelism and synchronization. Motion compensation, inverse quantization, and inverse transform modules are further optimized with SSE4 SIMD instructions. Simulations on a desktop with an Intel i7 processor 2600 running at 3.4 GHz show that the parallel SHVC software decoder is able to decode 1080p spatial 2x at up to 60 fps (frames per second) and 1080p spatial 1.5x at up to 50 fps for those bitstreams generated with SHVC common test conditions in the JCT-VC standardization group. The decoding performance at various bitrates with different optimization technologies and different numbers of threads are compared in terms of decoding speed and resource usage, including processor and memory.

Fast and Scalable Computation of the Forward and Inverse Discrete Periodic Radon Transform.

PubMed

Carranza, Cesar; Llamocca, Daniel; Pattichis, Marios

2016-01-01

The discrete periodic radon transform (DPRT) has extensively been used in applications that involve image reconstructions from projections. Beyond classic applications, the DPRT can also be used to compute fast convolutions that avoids the use of floating-point arithmetic associated with the use of the fast Fourier transform. Unfortunately, the use of the DPRT has been limited by the need to compute a large number of additions and the need for a large number of memory accesses. This paper introduces a fast and scalable approach for computing the forward and inverse DPRT that is based on the use of: a parallel array of fixed-point adder trees; circular shift registers to remove the need for accessing external memory components when selecting the input data for the adder trees; an image block-based approach to DPRT computation that can fit the proposed architecture to available resources; and fast transpositions that are computed in one or a few clock cycles that do not depend on the size of the input image. As a result, for an N × N image (N prime), the proposed approach can compute up to N(2) additions per clock cycle. Compared with the previous approaches, the scalable approach provides the fastest known implementations for different amounts of computational resources. For example, for a 251×251 image, for approximately 25% fewer flip-flops than required for a systolic implementation, we have that the scalable DPRT is computed 36 times faster. For the fastest case, we introduce optimized just 2N + ⌈log(2) N⌉ + 1 and 2N + 3 ⌈log(2) N⌉ + B + 2 cycles, architectures that can compute the DPRT and its inverse in respectively, where B is the number of bits used to represent each input pixel. On the other hand, the scalable DPRT approach requires more 1-b additions than for the systolic implementation and provides a tradeoff between speed and additional 1-b additions. All of the proposed DPRT architectures were implemented in VHSIC Hardware Description Language (VHDL) and validated using an Field-Programmable Gate Array (FPGA) implementation.

Space Situational Awareness Data Processing Scalability Utilizing Google Cloud Services

NASA Astrophysics Data System (ADS)

Greenly, D.; Duncan, M.; Wysack, J.; Flores, F.

Space Situational Awareness (SSA) is a fundamental and critical component of current space operations. The term SSA encompasses the awareness, understanding and predictability of all objects in space. As the population of orbital space objects and debris increases, the number of collision avoidance maneuvers grows and prompts the need for accurate and timely process measures. The SSA mission continually evolves to near real-time assessment and analysis demanding the need for higher processing capabilities. By conventional methods, meeting these demands requires the integration of new hardware to keep pace with the growing complexity of maneuver planning algorithms. SpaceNav has implemented a highly scalable architecture that will track satellites and debris by utilizing powerful virtual machines on the Google Cloud Platform. SpaceNav algorithms for processing CDMs outpace conventional means. A robust processing environment for tracking data, collision avoidance maneuvers and various other aspects of SSA can be created and deleted on demand. Migrating SpaceNav tools and algorithms into the Google Cloud Platform will be discussed and the trials and tribulations involved. Information will be shared on how and why certain cloud products were used as well as integration techniques that were implemented. Key items to be presented are: 1.Scientific algorithms and SpaceNav tools integrated into a scalable architecture a) Maneuver Planning b) Parallel Processing c) Monte Carlo Simulations d) Optimization Algorithms e) SW Application Development/Integration into the Google Cloud Platform 2. Compute Engine Processing a) Application Engine Automated Processing b) Performance testing and Performance Scalability c) Cloud MySQL databases and Database Scalability d) Cloud Data Storage e) Redundancy and Availability

Using domain decomposition in the multigrid NAS parallel benchmark on the Fujitsu VPP500

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wang, J.C.H.; Lung, H.; Katsumata, Y.

1995-12-01

In this paper, we demonstrate how domain decomposition can be applied to the multigrid algorithm to convert the code for MPP architectures. We also discuss the performance and scalability of this implementation on the new product line of Fujitsu`s vector parallel computer, VPP500. This computer has Fujitsu`s well-known vector processor as the PE each rated at 1.6 C FLOPS. The high speed crossbar network rated at 800 MB/s provides the inter-PE communication. The results show that the physical domain decomposition is the best way to solve MG problems on VPP500.

FoSSI: the family of simplified solver interfaces for the rapid development of parallel numerical atmosphere and ocean models

NASA Astrophysics Data System (ADS)

Frickenhaus, Stephan; Hiller, Wolfgang; Best, Meike

The portable software FoSSI is introduced that—in combination with additional free solver software packages—allows for an efficient and scalable parallel solution of large sparse linear equations systems arising in finite element model codes. FoSSI is intended to support rapid model code development, completely hiding the complexity of the underlying solver packages. In particular, the model developer need not be an expert in parallelization and is yet free to switch between different solver packages by simple modifications of the interface call. FoSSI offers an efficient and easy, yet flexible interface to several parallel solvers, most of them available on the web, such as PETSC, AZTEC, MUMPS, PILUT and HYPRE. FoSSI makes use of the concept of handles for vectors, matrices, preconditioners and solvers, that is frequently used in solver libraries. Hence, FoSSI allows for a flexible treatment of several linear equations systems and associated preconditioners at the same time, even in parallel on separate MPI-communicators. The second special feature in FoSSI is the task specifier, being a combination of keywords, each configuring a certain phase in the solver setup. This enables the user to control a solver over one unique subroutine. Furthermore, FoSSI has rather similar features for all solvers, making a fast solver intercomparison or exchange an easy task. FoSSI is a community software, proven in an adaptive 2D-atmosphere model and a 3D-primitive equation ocean model, both formulated in finite elements. The present paper discusses perspectives of an OpenMP-implementation of parallel iterative solvers based on domain decomposition methods. This approach to OpenMP solvers is rather attractive, as the code for domain-local operations of factorization, preconditioning and matrix-vector product can be readily taken from a sequential implementation that is also suitable to be used in an MPI-variant. Code development in this direction is in an advanced state under the name ScOPES: the Scalable Open Parallel sparse linear Equations Solver.

Performance Modeling and Measurement of Parallelized Code for Distributed Shared Memory Multiprocessors

NASA Technical Reports Server (NTRS)

Waheed, Abdul; Yan, Jerry

1998-01-01

This paper presents a model to evaluate the performance and overhead of parallelizing sequential code using compiler directives for multiprocessing on distributed shared memory (DSM) systems. With increasing popularity of shared address space architectures, it is essential to understand their performance impact on programs that benefit from shared memory multiprocessing. We present a simple model to characterize the performance of programs that are parallelized using compiler directives for shared memory multiprocessing. We parallelized the sequential implementation of NAS benchmarks using native Fortran77 compiler directives for an Origin2000, which is a DSM system based on a cache-coherent Non Uniform Memory Access (ccNUMA) architecture. We report measurement based performance of these parallelized benchmarks from four perspectives: efficacy of parallelization process; scalability; parallelization overhead; and comparison with hand-parallelized and -optimized version of the same benchmarks. Our results indicate that sequential programs can conveniently be parallelized for DSM systems using compiler directives but realizing performance gains as predicted by the performance model depends primarily on minimizing architecture-specific data locality overhead.

A General-purpose Framework for Parallel Processing of Large-scale LiDAR Data

NASA Astrophysics Data System (ADS)

Li, Z.; Hodgson, M.; Li, W.

2016-12-01

Light detection and ranging (LiDAR) technologies have proven efficiency to quickly obtain very detailed Earth surface data for a large spatial extent. Such data is important for scientific discoveries such as Earth and ecological sciences and natural disasters and environmental applications. However, handling LiDAR data poses grand geoprocessing challenges due to data intensity and computational intensity. Previous studies received notable success on parallel processing of LiDAR data to these challenges. However, these studies either relied on high performance computers and specialized hardware (GPUs) or focused mostly on finding customized solutions for some specific algorithms. We developed a general-purpose scalable framework coupled with sophisticated data decomposition and parallelization strategy to efficiently handle big LiDAR data. Specifically, 1) a tile-based spatial index is proposed to manage big LiDAR data in the scalable and fault-tolerable Hadoop distributed file system, 2) two spatial decomposition techniques are developed to enable efficient parallelization of different types of LiDAR processing tasks, and 3) by coupling existing LiDAR processing tools with Hadoop, this framework is able to conduct a variety of LiDAR data processing tasks in parallel in a highly scalable distributed computing environment. The performance and scalability of the framework is evaluated with a series of experiments conducted on a real LiDAR dataset using a proof-of-concept prototype system. The results show that the proposed framework 1) is able to handle massive LiDAR data more efficiently than standalone tools; and 2) provides almost linear scalability in terms of either increased workload (data volume) or increased computing nodes with both spatial decomposition strategies. We believe that the proposed framework provides valuable references on developing a collaborative cyberinfrastructure for processing big earth science data in a highly scalable environment.

A high performance parallel computing architecture for robust image features

NASA Astrophysics Data System (ADS)

Zhou, Renyan; Liu, Leibo; Wei, Shaojun

2014-03-01

A design of parallel architecture for image feature detection and description is proposed in this article. The major component of this architecture is a 2D cellular network composed of simple reprogrammable processors, enabling the Hessian Blob Detector and Haar Response Calculation, which are the most computing-intensive stage of the Speeded Up Robust Features (SURF) algorithm. Combining this 2D cellular network and dedicated hardware for SURF descriptors, this architecture achieves real-time image feature detection with minimal software in the host processor. A prototype FPGA implementation of the proposed architecture achieves 1318.9 GOPS general pixel processing @ 100 MHz clock and achieves up to 118 fps in VGA (640 × 480) image feature detection. The proposed architecture is stand-alone and scalable so it is easy to be migrated into VLSI implementation.

Massively parallel implementation of 3D-RISM calculation with volumetric 3D-FFT.

PubMed

Maruyama, Yutaka; Yoshida, Norio; Tadano, Hiroto; Takahashi, Daisuke; Sato, Mitsuhisa; Hirata, Fumio

2014-07-05

A new three-dimensional reference interaction site model (3D-RISM) program for massively parallel machines combined with the volumetric 3D fast Fourier transform (3D-FFT) was developed, and tested on the RIKEN K supercomputer. The ordinary parallel 3D-RISM program has a limitation on the number of parallelizations because of the limitations of the slab-type 3D-FFT. The volumetric 3D-FFT relieves this limitation drastically. We tested the 3D-RISM calculation on the large and fine calculation cell (2048(3) grid points) on 16,384 nodes, each having eight CPU cores. The new 3D-RISM program achieved excellent scalability to the parallelization, running on the RIKEN K supercomputer. As a benchmark application, we employed the program, combined with molecular dynamics simulation, to analyze the oligomerization process of chymotrypsin Inhibitor 2 mutant. The results demonstrate that the massive parallel 3D-RISM program is effective to analyze the hydration properties of the large biomolecular systems. Copyright © 2014 Wiley Periodicals, Inc.

Albany/FELIX: A parallel, scalable and robust, finite element, first-order Stokes approximation ice sheet solver built for advanced analysis

DOE PAGES

Tezaur, I. K.; Perego, M.; Salinger, A. G.; ...

2015-04-27

This paper describes a new parallel, scalable and robust finite element based solver for the first-order Stokes momentum balance equations for ice flow. The solver, known as Albany/FELIX, is constructed using the component-based approach to building application codes, in which mature, modular libraries developed as a part of the Trilinos project are combined using abstract interfaces and template-based generic programming, resulting in a final code with access to dozens of algorithmic and advanced analysis capabilities. Following an overview of the relevant partial differential equations and boundary conditions, the numerical methods chosen to discretize the ice flow equations are described, alongmore » with their implementation. The results of several verification studies of the model accuracy are presented using (1) new test cases for simplified two-dimensional (2-D) versions of the governing equations derived using the method of manufactured solutions, and (2) canonical ice sheet modeling benchmarks. Model accuracy and convergence with respect to mesh resolution are then studied on problems involving a realistic Greenland ice sheet geometry discretized using hexahedral and tetrahedral meshes. Also explored as a part of this study is the effect of vertical mesh resolution on the solution accuracy and solver performance. The robustness and scalability of our solver on these problems is demonstrated. Lastly, we show that good scalability can be achieved by preconditioning the iterative linear solver using a new algebraic multilevel preconditioner, constructed based on the idea of semi-coarsening.« less

Parallel auto-correlative statistics with VTK.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Pebay, Philippe Pierre; Bennett, Janine Camille

2013-08-01

This report summarizes existing statistical engines in VTK and presents both the serial and parallel auto-correlative statistics engines. It is a sequel to [PT08, BPRT09b, PT09, BPT09, PT10] which studied the parallel descriptive, correlative, multi-correlative, principal component analysis, contingency, k-means, and order statistics engines. The ease of use of the new parallel auto-correlative statistics engine is illustrated by the means of C++ code snippets and algorithm verification is provided. This report justifies the design of the statistics engines with parallel scalability in mind, and provides scalability and speed-up analysis results for the autocorrelative statistics engine.

A parallel computational model for GATE simulations.

PubMed

Rannou, F R; Vega-Acevedo, N; El Bitar, Z

2013-12-01

GATE/Geant4 Monte Carlo simulations are computationally demanding applications, requiring thousands of processor hours to produce realistic results. The classical strategy of distributing the simulation of individual events does not apply efficiently for Positron Emission Tomography (PET) experiments, because it requires a centralized coincidence processing and large communication overheads. We propose a parallel computational model for GATE that handles event generation and coincidence processing in a simple and efficient way by decentralizing event generation and processing but maintaining a centralized event and time coordinator. The model is implemented with the inclusion of a new set of factory classes that can run the same executable in sequential or parallel mode. A Mann-Whitney test shows that the output produced by this parallel model in terms of number of tallies is equivalent (but not equal) to its sequential counterpart. Computational performance evaluation shows that the software is scalable and well balanced. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

3D Data Denoising via Nonlocal Means Filter by Using Parallel GPU Strategies

PubMed Central

Cuomo, Salvatore; De Michele, Pasquale; Piccialli, Francesco

2014-01-01

Nonlocal Means (NLM) algorithm is widely considered as a state-of-the-art denoising filter in many research fields. Its high computational complexity leads researchers to the development of parallel programming approaches and the use of massively parallel architectures such as the GPUs. In the recent years, the GPU devices had led to achieving reasonable running times by filtering, slice-by-slice, and 3D datasets with a 2D NLM algorithm. In our approach we design and implement a fully 3D NonLocal Means parallel approach, adopting different algorithm mapping strategies on GPU architecture and multi-GPU framework, in order to demonstrate its high applicability and scalability. The experimental results we obtained encourage the usability of our approach in a large spectrum of applicative scenarios such as magnetic resonance imaging (MRI) or video sequence denoising. PMID:25045397

Effects of Ordering Strategies and Programming Paradigms on Sparse Matrix Computations

NASA Technical Reports Server (NTRS)

Oliker, Leonid; Li, Xiaoye; Husbands, Parry; Biswas, Rupak; Biegel, Bryan (Technical Monitor)

2002-01-01

The Conjugate Gradient (CG) algorithm is perhaps the best-known iterative technique to solve sparse linear systems that are symmetric and positive definite. For systems that are ill-conditioned, it is often necessary to use a preconditioning technique. In this paper, we investigate the effects of various ordering and partitioning strategies on the performance of parallel CG and ILU(O) preconditioned CG (PCG) using different programming paradigms and architectures. Results show that for this class of applications: ordering significantly improves overall performance on both distributed and distributed shared-memory systems, that cache reuse may be more important than reducing communication, that it is possible to achieve message-passing performance using shared-memory constructs through careful data ordering and distribution, and that a hybrid MPI+OpenMP paradigm increases programming complexity with little performance gains. A implementation of CG on the Cray MTA does not require special ordering or partitioning to obtain high efficiency and scalability, giving it a distinct advantage for adaptive applications; however, it shows limited scalability for PCG due to a lack of thread level parallelism.

Massively parallel first-principles simulation of electron dynamics in materials

DOE PAGES

Draeger, Erik W.; Andrade, Xavier; Gunnels, John A.; ...

2017-08-01

Here we present a highly scalable, parallel implementation of first-principles electron dynamics coupled with molecular dynamics (MD). By using optimized kernels, network topology aware communication, and by fully distributing all terms in the time-dependent Kohn–Sham equation, we demonstrate unprecedented time to solution for disordered aluminum systems of 2000 atoms (22,000 electrons) and 5400 atoms (59,400 electrons), with wall clock time as low as 7.5 s per MD time step. Despite a significant amount of non-local communication required in every iteration, we achieved excellent strong scaling and sustained performance on the Sequoia Blue Gene/Q supercomputer at LLNL. We obtained up tomore » 59% of the theoretical sustained peak performance on 16,384 nodes and performance of 8.75 Petaflop/s (43% of theoretical peak) on the full 98,304 node machine (1,572,864 cores). Lastly, scalable explicit electron dynamics allows for the study of phenomena beyond the reach of standard first-principles MD, in particular, materials subject to strong or rapid perturbations, such as pulsed electromagnetic radiation, particle irradiation, or strong electric currents.« less

Knowledge Support and Automation for Performance Analysis with PerfExplorer 2.0

DOE PAGES

Huck, Kevin A.; Malony, Allen D.; Shende, Sameer; ...

2008-01-01

The integration of scalable performance analysis in parallel development tools is difficult. The potential size of data sets and the need to compare results from multiple experiments presents a challenge to manage and process the information. Simply to characterize the performance of parallel applications running on potentially hundreds of thousands of processor cores requires new scalable analysis techniques. Furthermore, many exploratory analysis processes are repeatable and could be automated, but are now implemented as manual procedures. In this paper, we will discuss the current version of PerfExplorer, a performance analysis framework which provides dimension reduction, clustering and correlation analysis ofmore » individual trails of large dimensions, and can perform relative performance analysis between multiple application executions. PerfExplorer analysis processes can be captured in the form of Python scripts, automating what would otherwise be time-consuming tasks. We will give examples of large-scale analysis results, and discuss the future development of the framework, including the encoding and processing of expert performance rules, and the increasing use of performance metadata.« less

Scalable Nonlinear Solvers for Fully Implicit Coupled Nuclear Fuel Modeling. Final Report

DOE Office of Scientific and Technical Information (OSTI.GOV)

Cai, Xiao-Chuan; Keyes, David; Yang, Chao

2014-09-29

The focus of the project is on the development and customization of some highly scalable domain decomposition based preconditioning techniques for the numerical solution of nonlinear, coupled systems of partial differential equations (PDEs) arising from nuclear fuel simulations. These high-order PDEs represent multiple interacting physical fields (for example, heat conduction, oxygen transport, solid deformation), each is modeled by a certain type of Cahn-Hilliard and/or Allen-Cahn equations. Most existing approaches involve a careful splitting of the fields and the use of field-by-field iterations to obtain a solution of the coupled problem. Such approaches have many advantages such as ease of implementationmore » since only single field solvers are needed, but also exhibit disadvantages. For example, certain nonlinear interactions between the fields may not be fully captured, and for unsteady problems, stable time integration schemes are difficult to design. In addition, when implemented on large scale parallel computers, the sequential nature of the field-by-field iterations substantially reduces the parallel efficiency. To overcome the disadvantages, fully coupled approaches have been investigated in order to obtain full physics simulations.« less

miTRATA: a web-based tool for microRNA Truncation and Tailing Analysis.

PubMed

Patel, Parth; Ramachandruni, S Deepthi; Kakrana, Atul; Nakano, Mayumi; Meyers, Blake C

2016-02-01

We describe miTRATA, the first web-based tool for microRNA Truncation and Tailing Analysis--the analysis of 3' modifications of microRNAs including the loss or gain of nucleotides relative to the canonical sequence. miTRATA is implemented in Python (version 3) and employs parallel processing modules to enhance its scalability when analyzing multiple small RNA (sRNA) sequencing datasets. It utilizes miRBase, currently version 21, as a source of known microRNAs for analysis. miTRATA notifies user(s) via email to download as well as visualize the results online. miTRATA's strengths lie in (i) its biologist-focused web interface, (ii) improved scalability via parallel processing and (iii) its uniqueness as a webtool to perform microRNA truncation and tailing analysis. miTRATA is developed in Python and PHP. It is available as a web-based application from https://wasabi.dbi.udel.edu/∼apps/ta/. meyers@dbi.udel.edu Supplementary data are available at Bioinformatics online. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Massively parallel first-principles simulation of electron dynamics in materials

DOE Office of Scientific and Technical Information (OSTI.GOV)

Draeger, Erik W.; Andrade, Xavier; Gunnels, John A.

Here we present a highly scalable, parallel implementation of first-principles electron dynamics coupled with molecular dynamics (MD). By using optimized kernels, network topology aware communication, and by fully distributing all terms in the time-dependent Kohn–Sham equation, we demonstrate unprecedented time to solution for disordered aluminum systems of 2000 atoms (22,000 electrons) and 5400 atoms (59,400 electrons), with wall clock time as low as 7.5 s per MD time step. Despite a significant amount of non-local communication required in every iteration, we achieved excellent strong scaling and sustained performance on the Sequoia Blue Gene/Q supercomputer at LLNL. We obtained up tomore » 59% of the theoretical sustained peak performance on 16,384 nodes and performance of 8.75 Petaflop/s (43% of theoretical peak) on the full 98,304 node machine (1,572,864 cores). Lastly, scalable explicit electron dynamics allows for the study of phenomena beyond the reach of standard first-principles MD, in particular, materials subject to strong or rapid perturbations, such as pulsed electromagnetic radiation, particle irradiation, or strong electric currents.« less

BCYCLIC: A parallel block tridiagonal matrix cyclic solver

NASA Astrophysics Data System (ADS)

Hirshman, S. P.; Perumalla, K. S.; Lynch, V. E.; Sanchez, R.

2010-09-01

A block tridiagonal matrix is factored with minimal fill-in using a cyclic reduction algorithm that is easily parallelized. Storage of the factored blocks allows the application of the inverse to multiple right-hand sides which may not be known at factorization time. Scalability with the number of block rows is achieved with cyclic reduction, while scalability with the block size is achieved using multithreaded routines (OpenMP, GotoBLAS) for block matrix manipulation. This dual scalability is a noteworthy feature of this new solver, as well as its ability to efficiently handle arbitrary (non-powers-of-2) block row and processor numbers. Comparison with a state-of-the art parallel sparse solver is presented. It is expected that this new solver will allow many physical applications to optimally use the parallel resources on current supercomputers. Example usage of the solver in magneto-hydrodynamic (MHD), three-dimensional equilibrium solvers for high-temperature fusion plasmas is cited.

Revisiting Parallel Cyclic Reduction and Parallel Prefix-Based Algorithms for Block Tridiagonal System of Equations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Seal, Sudip K; Perumalla, Kalyan S; Hirshman, Steven Paul

2013-01-01

Simulations that require solutions of block tridiagonal systems of equations rely on fast parallel solvers for runtime efficiency. Leading parallel solvers that are highly effective for general systems of equations, dense or sparse, are limited in scalability when applied to block tridiagonal systems. This paper presents scalability results as well as detailed analyses of two parallel solvers that exploit the special structure of block tridiagonal matrices to deliver superior performance, often by orders of magnitude. A rigorous analysis of their relative parallel runtimes is shown to reveal the existence of a critical block size that separates the parameter space spannedmore » by the number of block rows, the block size and the processor count, into distinct regions that favor one or the other of the two solvers. Dependence of this critical block size on the above parameters as well as on machine-specific constants is established. These formal insights are supported by empirical results on up to 2,048 cores of a Cray XT4 system. To the best of our knowledge, this is the highest reported scalability for parallel block tridiagonal solvers to date.« less

Ultrafast and scalable cone-beam CT reconstruction using MapReduce in a cloud computing environment.

PubMed

Meng, Bowen; Pratx, Guillem; Xing, Lei

2011-12-01

Four-dimensional CT (4DCT) and cone beam CT (CBCT) are widely used in radiation therapy for accurate tumor target definition and localization. However, high-resolution and dynamic image reconstruction is computationally demanding because of the large amount of data processed. Efficient use of these imaging techniques in the clinic requires high-performance computing. The purpose of this work is to develop a novel ultrafast, scalable and reliable image reconstruction technique for 4D CBCT∕CT using a parallel computing framework called MapReduce. We show the utility of MapReduce for solving large-scale medical physics problems in a cloud computing environment. In this work, we accelerated the Feldcamp-Davis-Kress (FDK) algorithm by porting it to Hadoop, an open-source MapReduce implementation. Gated phases from a 4DCT scans were reconstructed independently. Following the MapReduce formalism, Map functions were used to filter and backproject subsets of projections, and Reduce function to aggregate those partial backprojection into the whole volume. MapReduce automatically parallelized the reconstruction process on a large cluster of computer nodes. As a validation, reconstruction of a digital phantom and an acquired CatPhan 600 phantom was performed on a commercial cloud computing environment using the proposed 4D CBCT∕CT reconstruction algorithm. Speedup of reconstruction time is found to be roughly linear with the number of nodes employed. For instance, greater than 10 times speedup was achieved using 200 nodes for all cases, compared to the same code executed on a single machine. Without modifying the code, faster reconstruction is readily achievable by allocating more nodes in the cloud computing environment. Root mean square error between the images obtained using MapReduce and a single-threaded reference implementation was on the order of 10(-7). Our study also proved that cloud computing with MapReduce is fault tolerant: the reconstruction completed successfully with identical results even when half of the nodes were manually terminated in the middle of the process. An ultrafast, reliable and scalable 4D CBCT∕CT reconstruction method was developed using the MapReduce framework. Unlike other parallel computing approaches, the parallelization and speedup required little modification of the original reconstruction code. MapReduce provides an efficient and fault tolerant means of solving large-scale computing problems in a cloud computing environment.

Ultrafast and scalable cone-beam CT reconstruction using MapReduce in a cloud computing environment

PubMed Central

Meng, Bowen; Pratx, Guillem; Xing, Lei

2011-01-01

Purpose: Four-dimensional CT (4DCT) and cone beam CT (CBCT) are widely used in radiation therapy for accurate tumor target definition and localization. However, high-resolution and dynamic image reconstruction is computationally demanding because of the large amount of data processed. Efficient use of these imaging techniques in the clinic requires high-performance computing. The purpose of this work is to develop a novel ultrafast, scalable and reliable image reconstruction technique for 4D CBCT/CT using a parallel computing framework called MapReduce. We show the utility of MapReduce for solving large-scale medical physics problems in a cloud computing environment. Methods: In this work, we accelerated the Feldcamp–Davis–Kress (FDK) algorithm by porting it to Hadoop, an open-source MapReduce implementation. Gated phases from a 4DCT scans were reconstructed independently. Following the MapReduce formalism, Map functions were used to filter and backproject subsets of projections, and Reduce function to aggregate those partial backprojection into the whole volume. MapReduce automatically parallelized the reconstruction process on a large cluster of computer nodes. As a validation, reconstruction of a digital phantom and an acquired CatPhan 600 phantom was performed on a commercial cloud computing environment using the proposed 4D CBCT/CT reconstruction algorithm. Results: Speedup of reconstruction time is found to be roughly linear with the number of nodes employed. For instance, greater than 10 times speedup was achieved using 200 nodes for all cases, compared to the same code executed on a single machine. Without modifying the code, faster reconstruction is readily achievable by allocating more nodes in the cloud computing environment. Root mean square error between the images obtained using MapReduce and a single-threaded reference implementation was on the order of 10−7. Our study also proved that cloud computing with MapReduce is fault tolerant: the reconstruction completed successfully with identical results even when half of the nodes were manually terminated in the middle of the process. Conclusions: An ultrafast, reliable and scalable 4D CBCT/CT reconstruction method was developed using the MapReduce framework. Unlike other parallel computing approaches, the parallelization and speedup required little modification of the original reconstruction code. MapReduce provides an efficient and fault tolerant means of solving large-scale computing problems in a cloud computing environment. PMID:22149842

Development and application of numerical techniques for general-relativistic magnetohydrodynamics simulations of black hole accretion

NASA Astrophysics Data System (ADS)

White, Christopher Joseph

We describe the implementation of sophisticated numerical techniques for general-relativistic magnetohydrodynamics simulations in the Athena++ code framework. Improvements over many existing codes include the use of advanced Riemann solvers and of staggered-mesh constrained transport. Combined with considerations for computational performance and parallel scalability, these allow us to investigate black hole accretion flows with unprecedented accuracy. The capability of the code is demonstrated by exploring magnetically arrested disks.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lusk, Ewing; Butler, Ralph; Pieper, Steven C.

Here, we take a historical approach to our presentation of self-scheduled task parallelism, a programming model with its origins in early irregular and nondeterministic computations encountered in automated theorem proving and logic programming. We show how an extremely simple task model has evolved into a system, asynchronous dynamic load balancing (ADLB), and a scalable implementation capable of supporting sophisticated applications on today’s (and tomorrow’s) largest supercomputers; and we illustrate the use of ADLB with a Green’s function Monte Carlo application, a modern, mature nuclear physics code in production use. Our lesson is that by surrendering a certain amount of generalitymore » and thus applicability, a minimal programming model (in terms of its basic concepts and the size of its application programmer interface) can achieve extreme scalability without introducing complexity.« less

A complexity-scalable software-based MPEG-2 video encoder.

PubMed

Chen, Guo-bin; Lu, Xin-ning; Wang, Xing-guo; Liu, Ji-lin

2004-05-01

With the development of general-purpose processors (GPP) and video signal processing algorithms, it is possible to implement a software-based real-time video encoder on GPP, and its low cost and easy upgrade attract developers' interests to transfer video encoding from specialized hardware to more flexible software. In this paper, the encoding structure is set up first to support complexity scalability; then a lot of high performance algorithms are used on the key time-consuming modules in coding process; finally, at programming level, processor characteristics are considered to improve data access efficiency and processing parallelism. Other programming methods such as lookup table are adopted to reduce the computational complexity. Simulation results showed that these ideas could not only improve the global performance of video coding, but also provide great flexibility in complexity regulation.

Global interrupt and barrier networks

DOEpatents

Blumrich, Matthias A.; Chen, Dong; Coteus, Paul W.; Gara, Alan G.; Giampapa, Mark E; Heidelberger, Philip; Kopcsay, Gerard V.; Steinmacher-Burow, Burkhard D.; Takken, Todd E.

2008-10-28

A system and method for generating global asynchronous signals in a computing structure. Particularly, a global interrupt and barrier network is implemented that implements logic for generating global interrupt and barrier signals for controlling global asynchronous operations performed by processing elements at selected processing nodes of a computing structure in accordance with a processing algorithm; and includes the physical interconnecting of the processing nodes for communicating the global interrupt and barrier signals to the elements via low-latency paths. The global asynchronous signals respectively initiate interrupt and barrier operations at the processing nodes at times selected for optimizing performance of the processing algorithms. In one embodiment, the global interrupt and barrier network is implemented in a scalable, massively parallel supercomputing device structure comprising a plurality of processing nodes interconnected by multiple independent networks, with each node including one or more processing elements for performing computation or communication activity as required when performing parallel algorithm operations. One multiple independent network includes a global tree network for enabling high-speed global tree communications among global tree network nodes or sub-trees thereof. The global interrupt and barrier network may operate in parallel with the global tree network for providing global asynchronous sideband signals.

GPAW - massively parallel electronic structure calculations with Python-based software.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Enkovaara, J.; Romero, N.; Shende, S.

2011-01-01

Electronic structure calculations are a widely used tool in materials science and large consumer of supercomputing resources. Traditionally, the software packages for these kind of simulations have been implemented in compiled languages, where Fortran in its different versions has been the most popular choice. While dynamic, interpreted languages, such as Python, can increase the effciency of programmer, they cannot compete directly with the raw performance of compiled languages. However, by using an interpreted language together with a compiled language, it is possible to have most of the productivity enhancing features together with a good numerical performance. We have used thismore » approach in implementing an electronic structure simulation software GPAW using the combination of Python and C programming languages. While the chosen approach works well in standard workstations and Unix environments, massively parallel supercomputing systems can present some challenges in porting, debugging and profiling the software. In this paper we describe some details of the implementation and discuss the advantages and challenges of the combined Python/C approach. We show that despite the challenges it is possible to obtain good numerical performance and good parallel scalability with Python based software.« less

Monte Carlo MP2 on Many Graphical Processing Units.

PubMed

Doran, Alexander E; Hirata, So

2016-10-11

In the Monte Carlo second-order many-body perturbation (MC-MP2) method, the long sum-of-product matrix expression of the MP2 energy, whose literal evaluation may be poorly scalable, is recast into a single high-dimensional integral of functions of electron pair coordinates, which is evaluated by the scalable method of Monte Carlo integration. The sampling efficiency is further accelerated by the redundant-walker algorithm, which allows a maximal reuse of electron pairs. Here, a multitude of graphical processing units (GPUs) offers a uniquely ideal platform to expose multilevel parallelism: fine-grain data-parallelism for the redundant-walker algorithm in which millions of threads compute and share orbital amplitudes on each GPU; coarse-grain instruction-parallelism for near-independent Monte Carlo integrations on many GPUs with few and infrequent interprocessor communications. While the efficiency boost by the redundant-walker algorithm on central processing units (CPUs) grows linearly with the number of electron pairs and tends to saturate when the latter exceeds the number of orbitals, on a GPU it grows quadratically before it increases linearly and then eventually saturates at a much larger number of pairs. This is because the orbital constructions are nearly perfectly parallelized on a GPU and thus completed in a near-constant time regardless of the number of pairs. In consequence, an MC-MP2/cc-pVDZ calculation of a benzene dimer is 2700 times faster on 256 GPUs (using 2048 electron pairs) than on two CPUs, each with 8 cores (which can use only up to 256 pairs effectively). We also numerically determine that the cost to achieve a given relative statistical uncertainty in an MC-MP2 energy increases as O(n 3 ) or better with system size n, which may be compared with the O(n 5 ) scaling of the conventional implementation of deterministic MP2. We thus establish the scalability of MC-MP2 with both system and computer sizes.

A scalable geometric multigrid solver for nonsymmetric elliptic systems with application to variable-density flows

NASA Astrophysics Data System (ADS)

Esmaily, M.; Jofre, L.; Mani, A.; Iaccarino, G.

2018-03-01

A geometric multigrid algorithm is introduced for solving nonsymmetric linear systems resulting from the discretization of the variable density Navier-Stokes equations on nonuniform structured rectilinear grids and high-Reynolds number flows. The restriction operation is defined such that the resulting system on the coarser grids is symmetric, thereby allowing for the use of efficient smoother algorithms. To achieve an optimal rate of convergence, the sequence of interpolation and restriction operations are determined through a dynamic procedure. A parallel partitioning strategy is introduced to minimize communication while maintaining the load balance between all processors. To test the proposed algorithm, we consider two cases: 1) homogeneous isotropic turbulence discretized on uniform grids and 2) turbulent duct flow discretized on stretched grids. Testing the algorithm on systems with up to a billion unknowns shows that the cost varies linearly with the number of unknowns. This O (N) behavior confirms the robustness of the proposed multigrid method regarding ill-conditioning of large systems characteristic of multiscale high-Reynolds number turbulent flows. The robustness of our method to density variations is established by considering cases where density varies sharply in space by a factor of up to 104, showing its applicability to two-phase flow problems. Strong and weak scalability studies are carried out, employing up to 30,000 processors, to examine the parallel performance of our implementation. Excellent scalability of our solver is shown for a granularity as low as 104 to 105 unknowns per processor. At its tested peak throughput, it solves approximately 4 billion unknowns per second employing over 16,000 processors with a parallel efficiency higher than 50%.

Linear static structural and vibration analysis on high-performance computers

NASA Technical Reports Server (NTRS)

Baddourah, M. A.; Storaasli, O. O.; Bostic, S. W.

1993-01-01

Parallel computers offer the oppurtunity to significantly reduce the computation time necessary to analyze large-scale aerospace structures. This paper presents algorithms developed for and implemented on massively-parallel computers hereafter referred to as Scalable High-Performance Computers (SHPC), for the most computationally intensive tasks involved in structural analysis, namely, generation and assembly of system matrices, solution of systems of equations and calculation of the eigenvalues and eigenvectors. Results on SHPC are presented for large-scale structural problems (i.e. models for High-Speed Civil Transport). The goal of this research is to develop a new, efficient technique which extends structural analysis to SHPC and makes large-scale structural analyses tractable.

GraphReduce: Processing Large-Scale Graphs on Accelerator-Based Systems

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sengupta, Dipanjan; Song, Shuaiwen; Agarwal, Kapil

2015-11-15

Recent work on real-world graph analytics has sought to leverage the massive amount of parallelism offered by GPU devices, but challenges remain due to the inherent irregularity of graph algorithms and limitations in GPU-resident memory for storing large graphs. We present GraphReduce, a highly efficient and scalable GPU-based framework that operates on graphs that exceed the device’s internal memory capacity. GraphReduce adopts a combination of edge- and vertex-centric implementations of the Gather-Apply-Scatter programming model and operates on multiple asynchronous GPU streams to fully exploit the high degrees of parallelism in GPUs with efficient graph data movement between the host andmore » device.« less

Shared virtual memory and generalized speedup

NASA Technical Reports Server (NTRS)

Sun, Xian-He; Zhu, Jianping

1994-01-01

Generalized speedup is defined as parallel speed over sequential speed. The generalized speedup and its relation with other existing performance metrics, such as traditional speedup, efficiency, scalability, etc., are carefully studied. In terms of the introduced asymptotic speed, it was shown that the difference between the generalized speedup and the traditional speedup lies in the definition of the efficiency of uniprocessor processing, which is a very important issue in shared virtual memory machines. A scientific application was implemented on a KSR-1 parallel computer. Experimental and theoretical results show that the generalized speedup is distinct from the traditional speedup and provides a more reasonable measurement. In the study of different speedups, various causes of superlinear speedup are also presented.

Unstructured Adaptive Grid Computations on an Array of SMPs

NASA Technical Reports Server (NTRS)

Biswas, Rupak; Pramanick, Ira; Sohn, Andrew; Simon, Horst D.

1996-01-01

Dynamic load balancing is necessary for parallel adaptive methods to solve unsteady CFD problems on unstructured grids. We have presented such a dynamic load balancing framework called JOVE, in this paper. Results on a four-POWERnode POWER CHALLENGEarray demonstrated that load balancing gives significant performance improvements over no load balancing for such adaptive computations. The parallel speedup of JOVE, implemented using MPI on the POWER CHALLENCEarray, was significant, being as high as 31 for 32 processors. An implementation of JOVE that exploits 'an array of SMPS' architecture was also studied; this hybrid JOVE outperformed flat JOVE by up to 28% on the meshes and adaption models tested. With large, realistic meshes and actual flow-solver and adaption phases incorporated into JOVE, hybrid JOVE can be expected to yield significant advantage over flat JOVE, especially as the number of processors is increased, thus demonstrating the scalability of an array of SMPs architecture.

A Comparison of Three Programming Models for Adaptive Applications

NASA Technical Reports Server (NTRS)

Shan, Hong-Zhang; Singh, Jaswinder Pal; Oliker, Leonid; Biswa, Rupak; Kwak, Dochan (Technical Monitor)

2000-01-01

We study the performance and programming effort for two major classes of adaptive applications under three leading parallel programming models. We find that all three models can achieve scalable performance on the state-of-the-art multiprocessor machines. The basic parallel algorithms needed for different programming models to deliver their best performance are similar, but the implementations differ greatly, far beyond the fact of using explicit messages versus implicit loads/stores. Compared with MPI and SHMEM, CC-SAS (cache-coherent shared address space) provides substantial ease of programming at the conceptual and program orchestration level, which often leads to the performance gain. However it may also suffer from the poor spatial locality of physically distributed shared data on large number of processors. Our CC-SAS implementation of the PARMETIS partitioner itself runs faster than in the other two programming models, and generates more balanced result for our application.

Recent Progress on the Parallel Implementation of Moving-Body Overset Grid Schemes

NASA Technical Reports Server (NTRS)

Wissink, Andrew; Allen, Edwin (Technical Monitor)

1998-01-01

Viscous calculations about geometrically complex bodies in which there is relative motion between component parts is one of the most computationally demanding problems facing CFD researchers today. This presentation documents results from the first two years of a CHSSI-funded effort within the U.S. Army AFDD to develop scalable dynamic overset grid methods for unsteady viscous calculations with moving-body problems. The first pan of the presentation will focus on results from OVERFLOW-D1, a parallelized moving-body overset grid scheme that employs traditional Chimera methodology. The two processes that dominate the cost of such problems are the flow solution on each component and the intergrid connectivity solution. Parallel implementations of the OVERFLOW flow solver and DCF3D connectivity software are coupled with a proposed two-part static-dynamic load balancing scheme and tested on the IBM SP and Cray T3E multi-processors. The second part of the presentation will cover some recent results from OVERFLOW-D2, a new flow solver that employs Cartesian grids with various levels of refinement, facilitating solution adaption. A study of the parallel performance of the scheme on large distributed- memory multiprocessor computer architectures will be reported.

Extreme Performance Scalable Operating Systems Final Progress Report (July 1, 2008 - October 31, 2011)

DOE Office of Scientific and Technical Information (OSTI.GOV)

Malony, Allen D; Shende, Sameer

This is the final progress report for the FastOS (Phase 2) (FastOS-2) project with Argonne National Laboratory and the University of Oregon (UO). The project started at UO on July 1, 2008 and ran until April 30, 2010, at which time a six-month no-cost extension began. The FastOS-2 work at UO delivered excellent results in all research work areas: * scalable parallel monitoring * kernel-level performance measurement * parallel I/0 system measurement * large-scale and hybrid application performance measurement * onlne scalable performance data reduction and analysis * binary instrumentation

Tinker-HP: a massively parallel molecular dynamics package for multiscale simulations of large complex systems with advanced point dipole polarizable force fields.

PubMed

Lagardère, Louis; Jolly, Luc-Henri; Lipparini, Filippo; Aviat, Félix; Stamm, Benjamin; Jing, Zhifeng F; Harger, Matthew; Torabifard, Hedieh; Cisneros, G Andrés; Schnieders, Michael J; Gresh, Nohad; Maday, Yvon; Ren, Pengyu Y; Ponder, Jay W; Piquemal, Jean-Philip

2018-01-28

We present Tinker-HP, a massively MPI parallel package dedicated to classical molecular dynamics (MD) and to multiscale simulations, using advanced polarizable force fields (PFF) encompassing distributed multipoles electrostatics. Tinker-HP is an evolution of the popular Tinker package code that conserves its simplicity of use and its reference double precision implementation for CPUs. Grounded on interdisciplinary efforts with applied mathematics, Tinker-HP allows for long polarizable MD simulations on large systems up to millions of atoms. We detail in the paper the newly developed extension of massively parallel 3D spatial decomposition to point dipole polarizable models as well as their coupling to efficient Krylov iterative and non-iterative polarization solvers. The design of the code allows the use of various computer systems ranging from laboratory workstations to modern petascale supercomputers with thousands of cores. Tinker-HP proposes therefore the first high-performance scalable CPU computing environment for the development of next generation point dipole PFFs and for production simulations. Strategies linking Tinker-HP to Quantum Mechanics (QM) in the framework of multiscale polarizable self-consistent QM/MD simulations are also provided. The possibilities, performances and scalability of the software are demonstrated via benchmarks calculations using the polarizable AMOEBA force field on systems ranging from large water boxes of increasing size and ionic liquids to (very) large biosystems encompassing several proteins as well as the complete satellite tobacco mosaic virus and ribosome structures. For small systems, Tinker-HP appears to be competitive with the Tinker-OpenMM GPU implementation of Tinker. As the system size grows, Tinker-HP remains operational thanks to its access to distributed memory and takes advantage of its new algorithmic enabling for stable long timescale polarizable simulations. Overall, a several thousand-fold acceleration over a single-core computation is observed for the largest systems. The extension of the present CPU implementation of Tinker-HP to other computational platforms is discussed.

Massively parallel and linear-scaling algorithm for second-order Moller–Plesset perturbation theory applied to the study of supramolecular wires

DOE PAGES

Kjaergaard, Thomas; Baudin, Pablo; Bykov, Dmytro; ...

2016-11-16

Here, we present a scalable cross-platform hybrid MPI/OpenMP/OpenACC implementation of the Divide–Expand–Consolidate (DEC) formalism with portable performance on heterogeneous HPC architectures. The Divide–Expand–Consolidate formalism is designed to reduce the steep computational scaling of conventional many-body methods employed in electronic structure theory to linear scaling, while providing a simple mechanism for controlling the error introduced by this approximation. Our massively parallel implementation of this general scheme has three levels of parallelism, being a hybrid of the loosely coupled task-based parallelization approach and the conventional MPI +X programming model, where X is either OpenMP or OpenACC. We demonstrate strong and weak scalabilitymore » of this implementation on heterogeneous HPC systems, namely on the GPU-based Cray XK7 Titan supercomputer at the Oak Ridge National Laboratory. Using the “resolution of the identity second-order Moller–Plesset perturbation theory” (RI-MP2) as the physical model for simulating correlated electron motion, the linear-scaling DEC implementation is applied to 1-aza-adamantane-trione (AAT) supramolecular wires containing up to 40 monomers (2440 atoms, 6800 correlated electrons, 24 440 basis functions and 91 280 auxiliary functions). This represents the largest molecular system treated at the MP2 level of theory, demonstrating an efficient removal of the scaling wall pertinent to conventional quantum many-body methods.« less

SCORPIO: A Scalable Two-Phase Parallel I/O Library With Application To A Large Scale Subsurface Simulator

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sreepathi, Sarat; Sripathi, Vamsi; Mills, Richard T

2013-01-01

Inefficient parallel I/O is known to be a major bottleneck among scientific applications employed on supercomputers as the number of processor cores grows into the thousands. Our prior experience indicated that parallel I/O libraries such as HDF5 that rely on MPI-IO do not scale well beyond 10K processor cores, especially on parallel file systems (like Lustre) with single point of resource contention. Our previous optimization efforts for a massively parallel multi-phase and multi-component subsurface simulator (PFLOTRAN) led to a two-phase I/O approach at the application level where a set of designated processes participate in the I/O process by splitting themore » I/O operation into a communication phase and a disk I/O phase. The designated I/O processes are created by splitting the MPI global communicator into multiple sub-communicators. The root process in each sub-communicator is responsible for performing the I/O operations for the entire group and then distributing the data to rest of the group. This approach resulted in over 25X speedup in HDF I/O read performance and 3X speedup in write performance for PFLOTRAN at over 100K processor cores on the ORNL Jaguar supercomputer. This research describes the design and development of a general purpose parallel I/O library, SCORPIO (SCalable block-ORiented Parallel I/O) that incorporates our optimized two-phase I/O approach. The library provides a simplified higher level abstraction to the user, sitting atop existing parallel I/O libraries (such as HDF5) and implements optimized I/O access patterns that can scale on larger number of processors. Performance results with standard benchmark problems and PFLOTRAN indicate that our library is able to maintain the same speedups as before with the added flexibility of being applicable to a wider range of I/O intensive applications.« less

SQDFT: Spectral Quadrature method for large-scale parallel O(N) Kohn-Sham calculations at high temperature

NASA Astrophysics Data System (ADS)

Suryanarayana, Phanish; Pratapa, Phanisri P.; Sharma, Abhiraj; Pask, John E.

2018-03-01

We present SQDFT: a large-scale parallel implementation of the Spectral Quadrature (SQ) method for O(N) Kohn-Sham Density Functional Theory (DFT) calculations at high temperature. Specifically, we develop an efficient and scalable finite-difference implementation of the infinite-cell Clenshaw-Curtis SQ approach, in which results for the infinite crystal are obtained by expressing quantities of interest as bilinear forms or sums of bilinear forms, that are then approximated by spatially localized Clenshaw-Curtis quadrature rules. We demonstrate the accuracy of SQDFT by showing systematic convergence of energies and atomic forces with respect to SQ parameters to reference diagonalization results, and convergence with discretization to established planewave results, for both metallic and insulating systems. We further demonstrate that SQDFT achieves excellent strong and weak parallel scaling on computer systems consisting of tens of thousands of processors, with near perfect O(N) scaling with system size and wall times as low as a few seconds per self-consistent field iteration. Finally, we verify the accuracy of SQDFT in large-scale quantum molecular dynamics simulations of aluminum at high temperature.

Empirical study of parallel LRU simulation algorithms

NASA Technical Reports Server (NTRS)

Carr, Eric; Nicol, David M.

1994-01-01

This paper reports on the performance of five parallel algorithms for simulating a fully associative cache operating under the LRU (Least-Recently-Used) replacement policy. Three of the algorithms are SIMD, and are implemented on the MasPar MP-2 architecture. Two other algorithms are parallelizations of an efficient serial algorithm on the Intel Paragon. One SIMD algorithm is quite simple, but its cost is linear in the cache size. The two other SIMD algorithm are more complex, but have costs that are independent on the cache size. Both the second and third SIMD algorithms compute all stack distances; the second SIMD algorithm is completely general, whereas the third SIMD algorithm presumes and takes advantage of bounds on the range of reference tags. Both MIMD algorithm implemented on the Paragon are general and compute all stack distances; they differ in one step that may affect their respective scalability. We assess the strengths and weaknesses of these algorithms as a function of problem size and characteristics, and compare their performance on traces derived from execution of three SPEC benchmark programs.

Adaptation of a Multi-Block Structured Solver for Effective Use in a Hybrid CPU/GPU Massively Parallel Environment

NASA Astrophysics Data System (ADS)

Gutzwiller, David; Gontier, Mathieu; Demeulenaere, Alain

2014-11-01

Multi-Block structured solvers hold many advantages over their unstructured counterparts, such as a smaller memory footprint and efficient serial performance. Historically, multi-block structured solvers have not been easily adapted for use in a High Performance Computing (HPC) environment, and the recent trend towards hybrid GPU/CPU architectures has further complicated the situation. This paper will elaborate on developments and innovations applied to the NUMECA FINE/Turbo solver that have allowed near-linear scalability with real-world problems on over 250 hybrid GPU/GPU cluster nodes. Discussion will focus on the implementation of virtual partitioning and load balancing algorithms using a novel meta-block concept. This implementation is transparent to the user, allowing all pre- and post-processing steps to be performed using a simple, unpartitioned grid topology. Additional discussion will elaborate on developments that have improved parallel performance, including fully parallel I/O with the ADIOS API and the GPU porting of the computationally heavy CPUBooster convergence acceleration module. Head of HPC and Release Management, Numeca International.

SNAVA-A real-time multi-FPGA multi-model spiking neural network simulation architecture.

PubMed

Sripad, Athul; Sanchez, Giovanny; Zapata, Mireya; Pirrone, Vito; Dorta, Taho; Cambria, Salvatore; Marti, Albert; Krishnamourthy, Karthikeyan; Madrenas, Jordi

2018-01-01

Spiking Neural Networks (SNN) for Versatile Applications (SNAVA) simulation platform is a scalable and programmable parallel architecture that supports real-time, large-scale, multi-model SNN computation. This parallel architecture is implemented in modern Field-Programmable Gate Arrays (FPGAs) devices to provide high performance execution and flexibility to support large-scale SNN models. Flexibility is defined in terms of programmability, which allows easy synapse and neuron implementation. This has been achieved by using a special-purpose Processing Elements (PEs) for computing SNNs, and analyzing and customizing the instruction set according to the processing needs to achieve maximum performance with minimum resources. The parallel architecture is interfaced with customized Graphical User Interfaces (GUIs) to configure the SNN's connectivity, to compile the neuron-synapse model and to monitor SNN's activity. Our contribution intends to provide a tool that allows to prototype SNNs faster than on CPU/GPU architectures but significantly cheaper than fabricating a customized neuromorphic chip. This could be potentially valuable to the computational neuroscience and neuromorphic engineering communities. Copyright © 2017 Elsevier Ltd. All rights reserved.

Hardware Implementation of Lossless Adaptive and Scalable Hyperspectral Data Compression for Space

NASA Technical Reports Server (NTRS)

Aranki, Nazeeh; Keymeulen, Didier; Bakhshi, Alireza; Klimesh, Matthew

2009-01-01

On-board lossless hyperspectral data compression reduces data volume in order to meet NASA and DoD limited downlink capabilities. The technique also improves signature extraction, object recognition and feature classification capabilities by providing exact reconstructed data on constrained downlink resources. At JPL a novel, adaptive and predictive technique for lossless compression of hyperspectral data was recently developed. This technique uses an adaptive filtering method and achieves a combination of low complexity and compression effectiveness that far exceeds state-of-the-art techniques currently in use. The JPL-developed 'Fast Lossless' algorithm requires no training data or other specific information about the nature of the spectral bands for a fixed instrument dynamic range. It is of low computational complexity and thus well-suited for implementation in hardware. A modified form of the algorithm that is better suited for data from pushbroom instruments is generally appropriate for flight implementation. A scalable field programmable gate array (FPGA) hardware implementation was developed. The FPGA implementation achieves a throughput performance of 58 Msamples/sec, which can be increased to over 100 Msamples/sec in a parallel implementation that uses twice the hardware resources This paper describes the hardware implementation of the 'Modified Fast Lossless' compression algorithm on an FPGA. The FPGA implementation targets the current state-of-the-art FPGAs (Xilinx Virtex IV and V families) and compresses one sample every clock cycle to provide a fast and practical real-time solution for space applications.

Parallel particle filters for online identification of mechanistic mathematical models of physiology from monitoring data: performance and real-time scalability in simulation scenarios.

PubMed

Zenker, Sven

2010-08-01

Combining mechanistic mathematical models of physiology with quantitative observations using probabilistic inference may offer advantages over established approaches to computerized decision support in acute care medicine. Particle filters (PF) can perform such inference successively as data becomes available. The potential of PF for real-time state estimation (SE) for a model of cardiovascular physiology is explored using parallel computers and the ability to achieve joint state and parameter estimation (JSPE) given minimal prior knowledge tested. A parallelized sequential importance sampling/resampling algorithm was implemented and its scalability for the pure SE problem for a non-linear five-dimensional ODE model of the cardiovascular system evaluated on a Cray XT3 using up to 1,024 cores. JSPE was implemented using a state augmentation approach with artificial stochastic evolution of the parameters. Its performance when simultaneously estimating the 5 states and 18 unknown parameters when given observations only of arterial pressure, central venous pressure, heart rate, and, optionally, cardiac output, was evaluated in a simulated bleeding/resuscitation scenario. SE was successful and scaled up to 1,024 cores with appropriate algorithm parametrization, with real-time equivalent performance for up to 10 million particles. JSPE in the described underdetermined scenario achieved excellent reproduction of observables and qualitative tracking of enddiastolic ventricular volumes and sympathetic nervous activity. However, only a subset of the posterior distributions of parameters concentrated around the true values for parts of the estimated trajectories. Parallelized PF's performance makes their application to complex mathematical models of physiology for the purpose of clinical data interpretation, prediction, and therapy optimization appear promising. JSPE in the described extremely underdetermined scenario nevertheless extracted information of potential clinical relevance from the data in this simulation setting. However, fully satisfactory resolution of this problem when minimal prior knowledge about parameter values is available will require further methodological improvements, which are discussed.

A massively parallel strategy for STR marker development, capture, and genotyping.

PubMed

Kistler, Logan; Johnson, Stephen M; Irwin, Mitchell T; Louis, Edward E; Ratan, Aakrosh; Perry, George H

2017-09-06

Short tandem repeat (STR) variants are highly polymorphic markers that facilitate powerful population genetic analyses. STRs are especially valuable in conservation and ecological genetic research, yielding detailed information on population structure and short-term demographic fluctuations. Massively parallel sequencing has not previously been leveraged for scalable, efficient STR recovery. Here, we present a pipeline for developing STR markers directly from high-throughput shotgun sequencing data without a reference genome, and an approach for highly parallel target STR recovery. We employed our approach to capture a panel of 5000 STRs from a test group of diademed sifakas (Propithecus diadema, n = 3), endangered Malagasy rainforest lemurs, and we report extremely efficient recovery of targeted loci-97.3-99.6% of STRs characterized with ≥10x non-redundant sequence coverage. We then tested our STR capture strategy on P. diadema fecal DNA, and report robust initial results and suggestions for future implementations. In addition to STR targets, this approach also generates large, genome-wide single nucleotide polymorphism (SNP) panels from flanking regions. Our method provides a cost-effective and scalable solution for rapid recovery of large STR and SNP datasets in any species without needing a reference genome, and can be used even with suboptimal DNA more easily acquired in conservation and ecological studies. Published by Oxford University Press on behalf of Nucleic Acids Research 2017.

Trainable hardware for dynamical computing using error backpropagation through physical media.

PubMed

Hermans, Michiel; Burm, Michaël; Van Vaerenbergh, Thomas; Dambre, Joni; Bienstman, Peter

2015-03-24

Neural networks are currently implemented on digital Von Neumann machines, which do not fully leverage their intrinsic parallelism. We demonstrate how to use a novel class of reconfigurable dynamical systems for analogue information processing, mitigating this problem. Our generic hardware platform for dynamic, analogue computing consists of a reciprocal linear dynamical system with nonlinear feedback. Thanks to reciprocity, a ubiquitous property of many physical phenomena like the propagation of light and sound, the error backpropagation-a crucial step for tuning such systems towards a specific task-can happen in hardware. This can potentially speed up the optimization process significantly, offering important benefits for the scalability of neuro-inspired hardware. In this paper, we show, using one experimentally validated and one conceptual example, that such systems may provide a straightforward mechanism for constructing highly scalable, fully dynamical analogue computers.

Trainable hardware for dynamical computing using error backpropagation through physical media

NASA Astrophysics Data System (ADS)

Hermans, Michiel; Burm, Michaël; van Vaerenbergh, Thomas; Dambre, Joni; Bienstman, Peter

2015-03-01

Neural networks are currently implemented on digital Von Neumann machines, which do not fully leverage their intrinsic parallelism. We demonstrate how to use a novel class of reconfigurable dynamical systems for analogue information processing, mitigating this problem. Our generic hardware platform for dynamic, analogue computing consists of a reciprocal linear dynamical system with nonlinear feedback. Thanks to reciprocity, a ubiquitous property of many physical phenomena like the propagation of light and sound, the error backpropagation—a crucial step for tuning such systems towards a specific task—can happen in hardware. This can potentially speed up the optimization process significantly, offering important benefits for the scalability of neuro-inspired hardware. In this paper, we show, using one experimentally validated and one conceptual example, that such systems may provide a straightforward mechanism for constructing highly scalable, fully dynamical analogue computers.

Scalable High Performance Computing: Direct and Large-Eddy Turbulent Flow Simulations Using Massively Parallel Computers

NASA Technical Reports Server (NTRS)

Morgan, Philip E.

2004-01-01

This final report contains reports of research related to the tasks "Scalable High Performance Computing: Direct and Lark-Eddy Turbulent FLow Simulations Using Massively Parallel Computers" and "Devleop High-Performance Time-Domain Computational Electromagnetics Capability for RCS Prediction, Wave Propagation in Dispersive Media, and Dual-Use Applications. The discussion of Scalable High Performance Computing reports on three objectives: validate, access scalability, and apply two parallel flow solvers for three-dimensional Navier-Stokes flows; develop and validate a high-order parallel solver for Direct Numerical Simulations (DNS) and Large Eddy Simulation (LES) problems; and Investigate and develop a high-order Reynolds averaged Navier-Stokes turbulence model. The discussion of High-Performance Time-Domain Computational Electromagnetics reports on five objectives: enhancement of an electromagnetics code (CHARGE) to be able to effectively model antenna problems; utilize lessons learned in high-order/spectral solution of swirling 3D jets to apply to solving electromagnetics project; transition a high-order fluids code, FDL3DI, to be able to solve Maxwell's Equations using compact-differencing; develop and demonstrate improved radiation absorbing boundary conditions for high-order CEM; and extend high-order CEM solver to address variable material properties. The report also contains a review of work done by the systems engineer.

Scalable Domain Decomposed Monte Carlo Particle Transport

NASA Astrophysics Data System (ADS)

O'Brien, Matthew Joseph

In this dissertation, we present the parallel algorithms necessary to run domain decomposed Monte Carlo particle transport on large numbers of processors (millions of processors). Previous algorithms were not scalable, and the parallel overhead became more computationally costly than the numerical simulation. The main algorithms we consider are: • Domain decomposition of constructive solid geometry: enables extremely large calculations in which the background geometry is too large to fit in the memory of a single computational node. • Load Balancing: keeps the workload per processor as even as possible so the calculation runs efficiently. • Global Particle Find: if particles are on the wrong processor, globally resolve their locations to the correct processor based on particle coordinate and background domain. • Visualizing constructive solid geometry, sourcing particles, deciding that particle streaming communication is completed and spatial redecomposition. These algorithms are some of the most important parallel algorithms required for domain decomposed Monte Carlo particle transport. We demonstrate that our previous algorithms were not scalable, prove that our new algorithms are scalable, and run some of the algorithms up to 2 million MPI processes on the Sequoia supercomputer.

Parallel computing of physical maps--a comparative study in SIMD and MIMD parallelism.

PubMed

Bhandarkar, S M; Chirravuri, S; Arnold, J

1996-01-01

Ordering clones from a genomic library into physical maps of whole chromosomes presents a central computational problem in genetics. Chromosome reconstruction via clone ordering is usually isomorphic to the NP-complete Optimal Linear Arrangement problem. Parallel SIMD and MIMD algorithms for simulated annealing based on Markov chain distribution are proposed and applied to the problem of chromosome reconstruction via clone ordering. Perturbation methods and problem-specific annealing heuristics are proposed and described. The SIMD algorithms are implemented on a 2048 processor MasPar MP-2 system which is an SIMD 2-D toroidal mesh architecture whereas the MIMD algorithms are implemented on an 8 processor Intel iPSC/860 which is an MIMD hypercube architecture. A comparative analysis of the various SIMD and MIMD algorithms is presented in which the convergence, speedup, and scalability characteristics of the various algorithms are analyzed and discussed. On a fine-grained, massively parallel SIMD architecture with a low synchronization overhead such as the MasPar MP-2, a parallel simulated annealing algorithm based on multiple periodically interacting searches performs the best. For a coarse-grained MIMD architecture with high synchronization overhead such as the Intel iPSC/860, a parallel simulated annealing algorithm based on multiple independent searches yields the best results. In either case, distribution of clonal data across multiple processors is shown to exacerbate the tendency of the parallel simulated annealing algorithm to get trapped in a local optimum.

Improving the scalability of hyperspectral imaging applications on heterogeneous platforms using adaptive run-time data compression

NASA Astrophysics Data System (ADS)

Plaza, Antonio; Plaza, Javier; Paz, Abel

2010-10-01

Latest generation remote sensing instruments (called hyperspectral imagers) are now able to generate hundreds of images, corresponding to different wavelength channels, for the same area on the surface of the Earth. In previous work, we have reported that the scalability of parallel processing algorithms dealing with these high-dimensional data volumes is affected by the amount of data to be exchanged through the communication network of the system. However, large messages are common in hyperspectral imaging applications since processing algorithms are pixel-based, and each pixel vector to be exchanged through the communication network is made up of hundreds of spectral values. Thus, decreasing the amount of data to be exchanged could improve the scalability and parallel performance. In this paper, we propose a new framework based on intelligent utilization of wavelet-based data compression techniques for improving the scalability of a standard hyperspectral image processing chain on heterogeneous networks of workstations. This type of parallel platform is quickly becoming a standard in hyperspectral image processing due to the distributed nature of collected hyperspectral data as well as its flexibility and low cost. Our experimental results indicate that adaptive lossy compression can lead to improvements in the scalability of the hyperspectral processing chain without sacrificing analysis accuracy, even at sub-pixel precision levels.

GraphReduce: Large-Scale Graph Analytics on Accelerator-Based HPC Systems

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sengupta, Dipanjan; Agarwal, Kapil; Song, Shuaiwen

2015-09-30

Recent work on real-world graph analytics has sought to leverage the massive amount of parallelism offered by GPU devices, but challenges remain due to the inherent irregularity of graph algorithms and limitations in GPU-resident memory for storing large graphs. We present GraphReduce, a highly efficient and scalable GPU-based framework that operates on graphs that exceed the device’s internal memory capacity. GraphReduce adopts a combination of both edge- and vertex-centric implementations of the Gather-Apply-Scatter programming model and operates on multiple asynchronous GPU streams to fully exploit the high degrees of parallelism in GPUs with efficient graph data movement between the hostmore » and the device.« less

Support of Multidimensional Parallelism in the OpenMP Programming Model

NASA Technical Reports Server (NTRS)

Jin, Hao-Qiang; Jost, Gabriele

2003-01-01

OpenMP is the current standard for shared-memory programming. While providing ease of parallel programming, the OpenMP programming model also has limitations which often effect the scalability of applications. Examples for these limitations are work distribution and point-to-point synchronization among threads. We propose extensions to the OpenMP programming model which allow the user to easily distribute the work in multiple dimensions and synchronize the workflow among the threads. The proposed extensions include four new constructs and the associated runtime library. They do not require changes to the source code and can be implemented based on the existing OpenMP standard. We illustrate the concept in a prototype translator and test with benchmark codes and a cloud modeling code.

Parallel Programming Strategies for Irregular Adaptive Applications

NASA Technical Reports Server (NTRS)

Biswas, Rupak; Biegel, Bryan (Technical Monitor)

2001-01-01

Achieving scalable performance for dynamic irregular applications is eminently challenging. Traditional message-passing approaches have been making steady progress towards this goal; however, they suffer from complex implementation requirements. The use of a global address space greatly simplifies the programming task, but can degrade the performance for such computations. In this work, we examine two typical irregular adaptive applications, Dynamic Remeshing and N-Body, under competing programming methodologies and across various parallel architectures. The Dynamic Remeshing application simulates flow over an airfoil, and refines localized regions of the underlying unstructured mesh. The N-Body experiment models two neighboring Plummer galaxies that are about to undergo a merger. Both problems demonstrate dramatic changes in processor workloads and interprocessor communication with time; thus, dynamic load balancing is a required component.

Scalable Algorithms for Clustering Large Geospatiotemporal Data Sets on Manycore Architectures

NASA Astrophysics Data System (ADS)

Mills, R. T.; Hoffman, F. M.; Kumar, J.; Sreepathi, S.; Sripathi, V.

2016-12-01

The increasing availability of high-resolution geospatiotemporal data sets from sources such as observatory networks, remote sensing platforms, and computational Earth system models has opened new possibilities for knowledge discovery using data sets fused from disparate sources. Traditional algorithms and computing platforms are impractical for the analysis and synthesis of data sets of this size; however, new algorithmic approaches that can effectively utilize the complex memory hierarchies and the extremely high levels of available parallelism in state-of-the-art high-performance computing platforms can enable such analysis. We describe a massively parallel implementation of accelerated k-means clustering and some optimizations to boost computational intensity and utilization of wide SIMD lanes on state-of-the art multi- and manycore processors, including the second-generation Intel Xeon Phi ("Knights Landing") processor based on the Intel Many Integrated Core (MIC) architecture, which includes several new features, including an on-package high-bandwidth memory. We also analyze the code in the context of a few practical applications to the analysis of climatic and remotely-sensed vegetation phenology data sets, and speculate on some of the new applications that such scalable analysis methods may enable.

Optimization of atmospheric transport models on HPC platforms

NASA Astrophysics Data System (ADS)

de la Cruz, Raúl; Folch, Arnau; Farré, Pau; Cabezas, Javier; Navarro, Nacho; Cela, José María

2016-12-01

The performance and scalability of atmospheric transport models on high performance computing environments is often far from optimal for multiple reasons including, for example, sequential input and output, synchronous communications, work unbalance, memory access latency or lack of task overlapping. We investigate how different software optimizations and porting to non general-purpose hardware architectures improve code scalability and execution times considering, as an example, the FALL3D volcanic ash transport model. To this purpose, we implement the FALL3D model equations in the WARIS framework, a software designed from scratch to solve in a parallel and efficient way different geoscience problems on a wide variety of architectures. In addition, we consider further improvements in WARIS such as hybrid MPI-OMP parallelization, spatial blocking, auto-tuning and thread affinity. Considering all these aspects together, the FALL3D execution times for a realistic test case running on general-purpose cluster architectures (Intel Sandy Bridge) decrease by a factor between 7 and 40 depending on the grid resolution. Finally, we port the application to Intel Xeon Phi (MIC) and NVIDIA GPUs (CUDA) accelerator-based architectures and compare performance, cost and power consumption on all the architectures. Implications on time-constrained operational model configurations are discussed.

Parallel scalability and efficiency of vortex particle method for aeroelasticity analysis of bluff bodies

NASA Astrophysics Data System (ADS)

Tolba, Khaled Ibrahim; Morgenthal, Guido

2018-01-01

This paper presents an analysis of the scalability and efficiency of a simulation framework based on the vortex particle method. The code is applied for the numerical aerodynamic analysis of line-like structures. The numerical code runs on multicore CPU and GPU architectures using OpenCL framework. The focus of this paper is the analysis of the parallel efficiency and scalability of the method being applied to an engineering test case, specifically the aeroelastic response of a long-span bridge girder at the construction stage. The target is to assess the optimal configuration and the required computer architecture, such that it becomes feasible to efficiently utilise the method within the computational resources available for a regular engineering office. The simulations and the scalability analysis are performed on a regular gaming type computer.

Scalability of a Low-Cost Multi-Teraflop Linux Cluster for High-End Classical Atomistic and Quantum Mechanical Simulations

NASA Technical Reports Server (NTRS)

Kikuchi, Hideaki; Kalia, Rajiv K.; Nakano, Aiichiro; Vashishta, Priya; Shimojo, Fuyuki; Saini, Subhash

2003-01-01

Scalability of a low-cost, Intel Xeon-based, multi-Teraflop Linux cluster is tested for two high-end scientific applications: Classical atomistic simulation based on the molecular dynamics method and quantum mechanical calculation based on the density functional theory. These scalable parallel applications use space-time multiresolution algorithms and feature computational-space decomposition, wavelet-based adaptive load balancing, and spacefilling-curve-based data compression for scalable I/O. Comparative performance tests are performed on a 1,024-processor Linux cluster and a conventional higher-end parallel supercomputer, 1,184-processor IBM SP4. The results show that the performance of the Linux cluster is comparable to that of the SP4. We also study various effects, such as the sharing of memory and L2 cache among processors, on the performance.

A 4 μW/Ch analog front-end module with moderate inversion and power-scalable sampling operation for 3-D neural microsystems.

PubMed

Al-Ashmouny, Khaled M; Chang, Sun-Il; Yoon, Euisik

2012-10-01

We report an analog front-end prototype designed in 0.25 μm CMOS process for hybrid integration into 3-D neural recording microsystems. For scaling towards massive parallel neural recording, the prototype has investigated some critical circuit challenges in power, area, interface, and modularity. We achieved extremely low power consumption of 4 μW/channel, optimized energy efficiency using moderate inversion in low-noise amplifiers (K of 5.98 × 10⁸ or NEF of 2.9), and minimized asynchronous interface (only 2 per 16 channels) for command and data capturing. We also implemented adaptable operations including programmable-gain amplification, power-scalable sampling (up to 50 kS/s/channel), wide configuration range (9-bit) for programmable gain and bandwidth, and 5-bit site selection capability (selecting 16 out of 128 sites). The implemented front-end module has achieved a reduction in noise-energy-area product by a factor of 5-25 times as compared to the state-of-the-art analog front-end approaches reported to date.

High performance computing aspects of a dimension independent semi-Lagrangian discontinuous Galerkin code

NASA Astrophysics Data System (ADS)

Einkemmer, Lukas

2016-05-01

The recently developed semi-Lagrangian discontinuous Galerkin approach is used to discretize hyperbolic partial differential equations (usually first order equations). Since these methods are conservative, local in space, and able to limit numerical diffusion, they are considered a promising alternative to more traditional semi-Lagrangian schemes (which are usually based on polynomial or spline interpolation). In this paper, we consider a parallel implementation of a semi-Lagrangian discontinuous Galerkin method for distributed memory systems (so-called clusters). Both strong and weak scaling studies are performed on the Vienna Scientific Cluster 2 (VSC-2). In the case of weak scaling we observe a parallel efficiency above 0.8 for both two and four dimensional problems and up to 8192 cores. Strong scaling results show good scalability to at least 512 cores (we consider problems that can be run on a single processor in reasonable time). In addition, we study the scaling of a two dimensional Vlasov-Poisson solver that is implemented using the framework provided. All of the simulations are conducted in the context of worst case communication overhead; i.e., in a setting where the CFL (Courant-Friedrichs-Lewy) number increases linearly with the problem size. The framework introduced in this paper facilitates a dimension independent implementation of scientific codes (based on C++ templates) using both an MPI and a hybrid approach to parallelization. We describe the essential ingredients of our implementation.

A compact linear accelerator based on a scalable microelectromechanical-system RF-structure

DOE PAGES

Persaud, A.; Ji, Q.; Feinberg, E.; ...

2017-06-08

Here, a new approach for a compact radio-frequency (RF) accelerator structure is presented. The new accelerator architecture is based on the Multiple Electrostatic Quadrupole Array Linear Accelerator (MEQALAC) structure that was first developed in the 1980s. The MEQALAC utilized RF resonators producing the accelerating fields and providing for higher beam currents through parallel beamlets focused using arrays of electrostatic quadrupoles (ESQs). While the early work obtained ESQs with lateral dimensions on the order of a few centimeters, using a printed circuit board (PCB), we reduce the characteristic dimension to the millimeter regime, while massively scaling up the potential number ofmore » parallel beamlets. Using Microelectromechanical systems scalable fabrication approaches, we are working on further red ucing the characteristic dimension to the sub-millimeter regime. The technology is based on RF-acceleration components and ESQs implemented in the PCB or silicon wafers where each beamlet passes through beam apertures in the wafer. The complete accelerator is then assembled by stacking these wafers. This approach has the potential for fast and inexpensive batch fabrication of the components and flexibility in system design for application specific beam energies and currents. For prototyping the accelerator architecture, the components have been fabricated using the PCB. In this paper, we present proof of concept results of the principal components using the PCB: RF acceleration and ESQ focusing. Finally, ongoing developments on implementing components in silicon and scaling of the accelerator technology to high currents and beam energies are discussed.« less

A compact linear accelerator based on a scalable microelectromechanical-system RF-structure

NASA Astrophysics Data System (ADS)

Persaud, A.; Ji, Q.; Feinberg, E.; Seidl, P. A.; Waldron, W. L.; Schenkel, T.; Lal, A.; Vinayakumar, K. B.; Ardanuc, S.; Hammer, D. A.

2017-06-01

A new approach for a compact radio-frequency (RF) accelerator structure is presented. The new accelerator architecture is based on the Multiple Electrostatic Quadrupole Array Linear Accelerator (MEQALAC) structure that was first developed in the 1980s. The MEQALAC utilized RF resonators producing the accelerating fields and providing for higher beam currents through parallel beamlets focused using arrays of electrostatic quadrupoles (ESQs). While the early work obtained ESQs with lateral dimensions on the order of a few centimeters, using a printed circuit board (PCB), we reduce the characteristic dimension to the millimeter regime, while massively scaling up the potential number of parallel beamlets. Using Microelectromechanical systems scalable fabrication approaches, we are working on further reducing the characteristic dimension to the sub-millimeter regime. The technology is based on RF-acceleration components and ESQs implemented in the PCB or silicon wafers where each beamlet passes through beam apertures in the wafer. The complete accelerator is then assembled by stacking these wafers. This approach has the potential for fast and inexpensive batch fabrication of the components and flexibility in system design for application specific beam energies and currents. For prototyping the accelerator architecture, the components have been fabricated using the PCB. In this paper, we present proof of concept results of the principal components using the PCB: RF acceleration and ESQ focusing. Ongoing developments on implementing components in silicon and scaling of the accelerator technology to high currents and beam energies are discussed.

A compact linear accelerator based on a scalable microelectromechanical-system RF-structure.

PubMed

Persaud, A; Ji, Q; Feinberg, E; Seidl, P A; Waldron, W L; Schenkel, T; Lal, A; Vinayakumar, K B; Ardanuc, S; Hammer, D A

2017-06-01

A new approach for a compact radio-frequency (RF) accelerator structure is presented. The new accelerator architecture is based on the Multiple Electrostatic Quadrupole Array Linear Accelerator (MEQALAC) structure that was first developed in the 1980s. The MEQALAC utilized RF resonators producing the accelerating fields and providing for higher beam currents through parallel beamlets focused using arrays of electrostatic quadrupoles (ESQs). While the early work obtained ESQs with lateral dimensions on the order of a few centimeters, using a printed circuit board (PCB), we reduce the characteristic dimension to the millimeter regime, while massively scaling up the potential number of parallel beamlets. Using Microelectromechanical systems scalable fabrication approaches, we are working on further reducing the characteristic dimension to the sub-millimeter regime. The technology is based on RF-acceleration components and ESQs implemented in the PCB or silicon wafers where each beamlet passes through beam apertures in the wafer. The complete accelerator is then assembled by stacking these wafers. This approach has the potential for fast and inexpensive batch fabrication of the components and flexibility in system design for application specific beam energies and currents. For prototyping the accelerator architecture, the components have been fabricated using the PCB. In this paper, we present proof of concept results of the principal components using the PCB: RF acceleration and ESQ focusing. Ongoing developments on implementing components in silicon and scaling of the accelerator technology to high currents and beam energies are discussed.

Scalable parallel distance field construction for large-scale applications

DOE PAGES

Yu, Hongfeng; Xie, Jinrong; Ma, Kwan -Liu; ...

2015-10-01

Computing distance fields is fundamental to many scientific and engineering applications. Distance fields can be used to direct analysis and reduce data. In this paper, we present a highly scalable method for computing 3D distance fields on massively parallel distributed-memory machines. Anew distributed spatial data structure, named parallel distance tree, is introduced to manage the level sets of data and facilitate surface tracking overtime, resulting in significantly reduced computation and communication costs for calculating the distance to the surface of interest from any spatial locations. Our method supports several data types and distance metrics from real-world applications. We demonstrate itsmore » efficiency and scalability on state-of-the-art supercomputers using both large-scale volume datasets and surface models. We also demonstrate in-situ distance field computation on dynamic turbulent flame surfaces for a petascale combustion simulation. In conclusion, our work greatly extends the usability of distance fields for demanding applications.« less

Scalable Parallel Distance Field Construction for Large-Scale Applications.

PubMed

Yu, Hongfeng; Xie, Jinrong; Ma, Kwan-Liu; Kolla, Hemanth; Chen, Jacqueline H

2015-10-01

Computing distance fields is fundamental to many scientific and engineering applications. Distance fields can be used to direct analysis and reduce data. In this paper, we present a highly scalable method for computing 3D distance fields on massively parallel distributed-memory machines. A new distributed spatial data structure, named parallel distance tree, is introduced to manage the level sets of data and facilitate surface tracking over time, resulting in significantly reduced computation and communication costs for calculating the distance to the surface of interest from any spatial locations. Our method supports several data types and distance metrics from real-world applications. We demonstrate its efficiency and scalability on state-of-the-art supercomputers using both large-scale volume datasets and surface models. We also demonstrate in-situ distance field computation on dynamic turbulent flame surfaces for a petascale combustion simulation. Our work greatly extends the usability of distance fields for demanding applications.

Highly parallel implementation of non-adiabatic Ehrenfest molecular dynamics

NASA Astrophysics Data System (ADS)

Kanai, Yosuke; Schleife, Andre; Draeger, Erik; Anisimov, Victor; Correa, Alfredo

2014-03-01

While the adiabatic Born-Oppenheimer approximation tremendously lowers computational effort, many questions in modern physics, chemistry, and materials science require an explicit description of coupled non-adiabatic electron-ion dynamics. Electronic stopping, i.e. the energy transfer of a fast projectile atom to the electronic system of the target material, is a notorious example. We recently implemented real-time time-dependent density functional theory based on the plane-wave pseudopotential formalism in the Qbox/qb@ll codes. We demonstrate that explicit integration using a fourth-order Runge-Kutta scheme is very suitable for modern highly parallelized supercomputers. Applying the new implementation to systems with hundreds of atoms and thousands of electrons, we achieved excellent performance and scalability on a large number of nodes both on the BlueGene based ``Sequoia'' system at LLNL as well as the Cray architecture of ``Blue Waters'' at NCSA. As an example, we discuss our work on computing the electronic stopping power of aluminum and gold for hydrogen projectiles, showing an excellent agreement with experiment. These first-principles calculations allow us to gain important insight into the the fundamental physics of electronic stopping.

An Efficient Multicore Implementation of a Novel HSS-Structured Multifrontal Solver Using Randomized Sampling

DOE PAGES

Ghysels, Pieter; Li, Xiaoye S.; Rouet, Francois -Henry; ...

2016-10-27

Here, we present a sparse linear system solver that is based on a multifrontal variant of Gaussian elimination and exploits low-rank approximation of the resulting dense frontal matrices. We use hierarchically semiseparable (HSS) matrices, which have low-rank off-diagonal blocks, to approximate the frontal matrices. For HSS matrix construction, a randomized sampling algorithm is used together with interpolative decompositions. The combination of the randomized compression with a fast ULV HSS factoriz ation leads to a solver with lower computational complexity than the standard multifrontal method for many applications, resulting in speedups up to 7 fold for problems in our test suite.more » The implementation targets many-core systems by using task parallelism with dynamic runtime scheduling. Numerical experiments show performance improvements over state-of-the-art sparse direct solvers. The implementation achieves high performance and good scalability on a range of modern shared memory parallel systems, including the Intel Xeon Phi (MIC). The code is part of a software package called STRUMPACK - STRUctured Matrices PACKage, which also has a distributed memory component for dense rank-structured matrices.« less

A multi-platform evaluation of the randomized CX low-rank matrix factorization in Spark

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gittens, Alex; Kottalam, Jey; Yang, Jiyan

We investigate the performance and scalability of the randomized CX low-rank matrix factorization and demonstrate its applicability through the analysis of a 1TB mass spectrometry imaging (MSI) dataset, using Apache Spark on an Amazon EC2 cluster, a Cray XC40 system, and an experimental Cray cluster. We implemented this factorization both as a parallelized C implementation with hand-tuned optimizations and in Scala using the Apache Spark high-level cluster computing framework. We obtained consistent performance across the three platforms: using Spark we were able to process the 1TB size dataset in under 30 minutes with 960 cores on all systems, with themore » fastest times obtained on the experimental Cray cluster. In comparison, the C implementation was 21X faster on the Amazon EC2 system, due to careful cache optimizations, bandwidth-friendly access of matrices and vector computation using SIMD units. We report these results and their implications on the hardware and software issues arising in supporting data-centric workloads in parallel and distributed environments.« less

Porting AMG2013 to Heterogeneous CPU+GPU Nodes

DOE Office of Scientific and Technical Information (OSTI.GOV)

Samfass, Philipp

LLNL's future advanced technology system SIERRA will feature heterogeneous compute nodes that consist of IBM PowerV9 CPUs and NVIDIA Volta GPUs. Conceptually, the motivation for such an architecture is quite straightforward: While GPUs are optimized for throughput on massively parallel workloads, CPUs strive to minimize latency for rather sequential operations. Yet, making optimal use of heterogeneous architectures raises new challenges for the development of scalable parallel software, e.g., with respect to work distribution. Porting LLNL's parallel numerical libraries to upcoming heterogeneous CPU+GPU architectures is therefore a critical factor for ensuring LLNL's future success in ful lling its national mission. Onemore » of these libraries, called HYPRE, provides parallel solvers and precondi- tioners for large, sparse linear systems of equations. In the context of this intern- ship project, I consider AMG2013 which is a proxy application for major parts of HYPRE that implements a benchmark for setting up and solving di erent systems of linear equations. In the following, I describe in detail how I ported multiple parts of AMG2013 to the GPU (Section 2) and present results for di erent experiments that demonstrate a successful parallel implementation on the heterogeneous ma- chines surface and ray (Section 3). In Section 4, I give guidelines on how my code should be used. Finally, I conclude and give an outlook for future work (Section 5).« less

PRATHAM: Parallel Thermal Hydraulics Simulations using Advanced Mesoscopic Methods

DOE Office of Scientific and Technical Information (OSTI.GOV)

Joshi, Abhijit S; Jain, Prashant K; Mudrich, Jaime A

2012-01-01

At the Oak Ridge National Laboratory, efforts are under way to develop a 3D, parallel LBM code called PRATHAM (PaRAllel Thermal Hydraulic simulations using Advanced Mesoscopic Methods) to demonstrate the accuracy and scalability of LBM for turbulent flow simulations in nuclear applications. The code has been developed using FORTRAN-90, and parallelized using the message passing interface MPI library. Silo library is used to compact and write the data files, and VisIt visualization software is used to post-process the simulation data in parallel. Both the single relaxation time (SRT) and multi relaxation time (MRT) LBM schemes have been implemented in PRATHAM.more » To capture turbulence without prohibitively increasing the grid resolution requirements, an LES approach [5] is adopted allowing large scale eddies to be numerically resolved while modeling the smaller (subgrid) eddies. In this work, a Smagorinsky model has been used, which modifies the fluid viscosity by an additional eddy viscosity depending on the magnitude of the rate-of-strain tensor. In LBM, this is achieved by locally varying the relaxation time of the fluid.« less

Scalable load balancing for massively parallel distributed Monte Carlo particle transport

DOE Office of Scientific and Technical Information (OSTI.GOV)

O'Brien, M. J.; Brantley, P. S.; Joy, K. I.

2013-07-01

In order to run computer simulations efficiently on massively parallel computers with hundreds of thousands or millions of processors, care must be taken that the calculation is load balanced across the processors. Examining the workload of every processor leads to an unscalable algorithm, with run time at least as large as O(N), where N is the number of processors. We present a scalable load balancing algorithm, with run time 0(log(N)), that involves iterated processor-pair-wise balancing steps, ultimately leading to a globally balanced workload. We demonstrate scalability of the algorithm up to 2 million processors on the Sequoia supercomputer at Lawrencemore » Livermore National Laboratory. (authors)« less

Bioinformatics algorithm based on a parallel implementation of a machine learning approach using transducers

NASA Astrophysics Data System (ADS)

Roche-Lima, Abiel; Thulasiram, Ruppa K.

2012-02-01

Finite automata, in which each transition is augmented with an output label in addition to the familiar input label, are considered finite-state transducers. Transducers have been used to analyze some fundamental issues in bioinformatics. Weighted finite-state transducers have been proposed to pairwise alignments of DNA and protein sequences; as well as to develop kernels for computational biology. Machine learning algorithms for conditional transducers have been implemented and used for DNA sequence analysis. Transducer learning algorithms are based on conditional probability computation. It is calculated by using techniques, such as pair-database creation, normalization (with Maximum-Likelihood normalization) and parameters optimization (with Expectation-Maximization - EM). These techniques are intrinsically costly for computation, even worse when are applied to bioinformatics, because the databases sizes are large. In this work, we describe a parallel implementation of an algorithm to learn conditional transducers using these techniques. The algorithm is oriented to bioinformatics applications, such as alignments, phylogenetic trees, and other genome evolution studies. Indeed, several experiences were developed using the parallel and sequential algorithm on Westgrid (specifically, on the Breeze cluster). As results, we obtain that our parallel algorithm is scalable, because execution times are reduced considerably when the data size parameter is increased. Another experience is developed by changing precision parameter. In this case, we obtain smaller execution times using the parallel algorithm. Finally, number of threads used to execute the parallel algorithm on the Breezy cluster is changed. In this last experience, we obtain as result that speedup is considerably increased when more threads are used; however there is a convergence for number of threads equal to or greater than 16.

Parallel multigrid smoothing: polynomial versus Gauss-Seidel

NASA Astrophysics Data System (ADS)

Adams, Mark; Brezina, Marian; Hu, Jonathan; Tuminaro, Ray

2003-07-01

Gauss-Seidel is often the smoother of choice within multigrid applications. In the context of unstructured meshes, however, maintaining good parallel efficiency is difficult with multiplicative iterative methods such as Gauss-Seidel. This leads us to consider alternative smoothers. We discuss the computational advantages of polynomial smoothers within parallel multigrid algorithms for positive definite symmetric systems. Two particular polynomials are considered: Chebyshev and a multilevel specific polynomial. The advantages of polynomial smoothing over traditional smoothers such as Gauss-Seidel are illustrated on several applications: Poisson's equation, thin-body elasticity, and eddy current approximations to Maxwell's equations. While parallelizing the Gauss-Seidel method typically involves a compromise between a scalable convergence rate and maintaining high flop rates, polynomial smoothers achieve parallel scalable multigrid convergence rates without sacrificing flop rates. We show that, although parallel computers are the main motivation, polynomial smoothers are often surprisingly competitive with Gauss-Seidel smoothers on serial machines.

SUPREM-DSMC: A New Scalable, Parallel, Reacting, Multidimensional Direct Simulation Monte Carlo Flow Code

NASA Technical Reports Server (NTRS)

Campbell, David; Wysong, Ingrid; Kaplan, Carolyn; Mott, David; Wadsworth, Dean; VanGilder, Douglas

2000-01-01

An AFRL/NRL team has recently been selected to develop a scalable, parallel, reacting, multidimensional (SUPREM) Direct Simulation Monte Carlo (DSMC) code for the DoD user community under the High Performance Computing Modernization Office (HPCMO) Common High Performance Computing Software Support Initiative (CHSSI). This paper will introduce the JANNAF Exhaust Plume community to this three-year development effort and present the overall goals, schedule, and current status of this new code.

Implementation of the Timepix ASIC in the Scalable Readout System

NASA Astrophysics Data System (ADS)

Lupberger, M.; Desch, K.; Kaminski, J.

2016-09-01

We report on the development of electronics hardware, FPGA firmware and software to provide a flexible multi-chip readout of the Timepix ASIC within the framework of the Scalable Readout System (SRS). The system features FPGA-based zero-suppression and the possibility to read out up to 4×8 chips with a single Front End Concentrator (FEC). By operating several FECs in parallel, in principle an arbitrary number of chips can be read out, exploiting the scaling features of SRS. Specifically, we tested the system with a setup consisting of 160 Timepix ASICs, operated as GridPix devices in a large TPC field cage in a 1 T magnetic field at a DESY test beam facility providing an electron beam of up to 6 GeV. We discuss the design choices, the dedicated hardware components, the FPGA firmware as well as the performance of the system in the test beam.

Simplified Parallel Domain Traversal

DOE Office of Scientific and Technical Information (OSTI.GOV)

Erickson III, David J

2011-01-01

Many data-intensive scientific analysis techniques require global domain traversal, which over the years has been a bottleneck for efficient parallelization across distributed-memory architectures. Inspired by MapReduce and other simplified parallel programming approaches, we have designed DStep, a flexible system that greatly simplifies efficient parallelization of domain traversal techniques at scale. In order to deliver both simplicity to users as well as scalability on HPC platforms, we introduce a novel two-tiered communication architecture for managing and exploiting asynchronous communication loads. We also integrate our design with advanced parallel I/O techniques that operate directly on native simulation output. We demonstrate DStep bymore » performing teleconnection analysis across ensemble runs of terascale atmospheric CO{sub 2} and climate data, and we show scalability results on up to 65,536 IBM BlueGene/P cores.« less

A Smart Cage With Uniform Wireless Power Distribution in 3D for Enabling Long-Term Experiments With Freely Moving Animals.

PubMed

Mirbozorgi, S Abdollah; Bahrami, Hadi; Sawan, Mohamad; Gosselin, Benoit

2016-04-01

This paper presents a novel experimental chamber with uniform wireless power distribution in 3D for enabling long-term biomedical experiments with small freely moving animal subjects. The implemented power transmission chamber prototype is based on arrays of parallel resonators and multicoil inductive links, to form a novel and highly efficient wireless power transmission system. The power transmitter unit includes several identical resonators enclosed in a scalable array of overlapping square coils which are connected in parallel to provide uniform power distribution along x and y. Moreover, the proposed chamber uses two arrays of primary resonators, facing each other, and connected in parallel to achieve uniform power distribution along the z axis. Each surface includes 9 overlapped coils connected in parallel and implemented into two layers of FR4 printed circuit board. The chamber features a natural power localization mechanism, which simplifies its implementation and ease its operation by avoiding the need for active detection and control mechanisms. A single power surface based on the proposed approach can provide a power transfer efficiency (PTE) of 69% and a power delivered to the load (PDL) of 120 mW, for a separation distance of 4 cm, whereas the complete chamber prototype provides a uniform PTE of 59% and a PDL of 100 mW in 3D, everywhere inside the chamber with a size of 27×27×16 cm(3).

Scalability of Parallel Spatial Direct Numerical Simulations on Intel Hypercube and IBM SP1 and SP2

NASA Technical Reports Server (NTRS)

Joslin, Ronald D.; Hanebutte, Ulf R.; Zubair, Mohammad

1995-01-01

The implementation and performance of a parallel spatial direct numerical simulation (PSDNS) approach on the Intel iPSC/860 hypercube and IBM SP1 and SP2 parallel computers is documented. Spatially evolving disturbances associated with the laminar-to-turbulent transition in boundary-layer flows are computed with the PSDNS code. The feasibility of using the PSDNS to perform transition studies on these computers is examined. The results indicate that PSDNS approach can effectively be parallelized on a distributed-memory parallel machine by remapping the distributed data structure during the course of the calculation. Scalability information is provided to estimate computational costs to match the actual costs relative to changes in the number of grid points. By increasing the number of processors, slower than linear speedups are achieved with optimized (machine-dependent library) routines. This slower than linear speedup results because the computational cost is dominated by FFT routine, which yields less than ideal speedups. By using appropriate compile options and optimized library routines on the SP1, the serial code achieves 52-56 M ops on a single node of the SP1 (45 percent of theoretical peak performance). The actual performance of the PSDNS code on the SP1 is evaluated with a "real world" simulation that consists of 1.7 million grid points. One time step of this simulation is calculated on eight nodes of the SP1 in the same time as required by a Cray Y/MP supercomputer. For the same simulation, 32-nodes of the SP1 and SP2 are required to reach the performance of a Cray C-90. A 32 node SP1 (SP2) configuration is 2.9 (4.6) times faster than a Cray Y/MP for this simulation, while the hypercube is roughly 2 times slower than the Y/MP for this application. KEY WORDS: Spatial direct numerical simulations; incompressible viscous flows; spectral methods; finite differences; parallel computing.

SQDFT: Spectral Quadrature method for large-scale parallel O ( N ) Kohn–Sham calculations at high temperature

DOE Office of Scientific and Technical Information (OSTI.GOV)

Suryanarayana, Phanish; Pratapa, Phanisri P.; Sharma, Abhiraj

We present SQDFT: a large-scale parallel implementation of the Spectral Quadrature (SQ) method formore » $$\\mathscr{O}(N)$$ Kohn–Sham Density Functional Theory (DFT) calculations at high temperature. Specifically, we develop an efficient and scalable finite-difference implementation of the infinite-cell Clenshaw–Curtis SQ approach, in which results for the infinite crystal are obtained by expressing quantities of interest as bilinear forms or sums of bilinear forms, that are then approximated by spatially localized Clenshaw–Curtis quadrature rules. We demonstrate the accuracy of SQDFT by showing systematic convergence of energies and atomic forces with respect to SQ parameters to reference diagonalization results, and convergence with discretization to established planewave results, for both metallic and insulating systems. Here, we further demonstrate that SQDFT achieves excellent strong and weak parallel scaling on computer systems consisting of tens of thousands of processors, with near perfect $$\\mathscr{O}(N)$$ scaling with system size and wall times as low as a few seconds per self-consistent field iteration. Finally, we verify the accuracy of SQDFT in large-scale quantum molecular dynamics simulations of aluminum at high temperature.« less

SQDFT: Spectral Quadrature method for large-scale parallel O ( N ) Kohn–Sham calculations at high temperature

DOE PAGES

Suryanarayana, Phanish; Pratapa, Phanisri P.; Sharma, Abhiraj; ...

2017-12-07

We present SQDFT: a large-scale parallel implementation of the Spectral Quadrature (SQ) method formore » $$\\mathscr{O}(N)$$ Kohn–Sham Density Functional Theory (DFT) calculations at high temperature. Specifically, we develop an efficient and scalable finite-difference implementation of the infinite-cell Clenshaw–Curtis SQ approach, in which results for the infinite crystal are obtained by expressing quantities of interest as bilinear forms or sums of bilinear forms, that are then approximated by spatially localized Clenshaw–Curtis quadrature rules. We demonstrate the accuracy of SQDFT by showing systematic convergence of energies and atomic forces with respect to SQ parameters to reference diagonalization results, and convergence with discretization to established planewave results, for both metallic and insulating systems. Here, we further demonstrate that SQDFT achieves excellent strong and weak parallel scaling on computer systems consisting of tens of thousands of processors, with near perfect $$\\mathscr{O}(N)$$ scaling with system size and wall times as low as a few seconds per self-consistent field iteration. Finally, we verify the accuracy of SQDFT in large-scale quantum molecular dynamics simulations of aluminum at high temperature.« less

Fan-out Estimation in Spin-based Quantum Computer Scale-up.

PubMed

Nguyen, Thien; Hill, Charles D; Hollenberg, Lloyd C L; James, Matthew R

2017-10-17

Solid-state spin-based qubits offer good prospects for scaling based on their long coherence times and nexus to large-scale electronic scale-up technologies. However, high-threshold quantum error correction requires a two-dimensional qubit array operating in parallel, posing significant challenges in fabrication and control. While architectures incorporating distributed quantum control meet this challenge head-on, most designs rely on individual control and readout of all qubits with high gate densities. We analysed the fan-out routing overhead of a dedicated control line architecture, basing the analysis on a generalised solid-state spin qubit platform parameterised to encompass Coulomb confined (e.g. donor based spin qubits) or electrostatically confined (e.g. quantum dot based spin qubits) implementations. The spatial scalability under this model is estimated using standard electronic routing methods and present-day fabrication constraints. Based on reasonable assumptions for qubit control and readout we estimate 10 2 -10 5 physical qubits, depending on the quantum interconnect implementation, can be integrated and fanned-out independently. Assuming relatively long control-free interconnects the scalability can be extended. Ultimately, the universal quantum computation may necessitate a much higher number of integrated qubits, indicating that higher dimensional electronics fabrication and/or multiplexed distributed control and readout schemes may be the preferredstrategy for large-scale implementation.

Improving NASA's Multiscale Modeling Framework for Tropical Cyclone Climate Study

NASA Technical Reports Server (NTRS)

Shen, Bo-Wen; Nelson, Bron; Cheung, Samson; Tao, Wei-Kuo

2013-01-01

One of the current challenges in tropical cyclone (TC) research is how to improve our understanding of TC interannual variability and the impact of climate change on TCs. Recent advances in global modeling, visualization, and supercomputing technologies at NASA show potential for such studies. In this article, the authors discuss recent scalability improvement to the multiscale modeling framework (MMF) that makes it feasible to perform long-term TC-resolving simulations. The MMF consists of the finite-volume general circulation model (fvGCM), supplemented by a copy of the Goddard cumulus ensemble model (GCE) at each of the fvGCM grid points, giving 13,104 GCE copies. The original fvGCM implementation has a 1D data decomposition; the revised MMF implementation retains the 1D decomposition for most of the code, but uses a 2D decomposition for the massive copies of GCEs. Because the vast majority of computation time in the MMF is spent computing the GCEs, this approach can achieve excellent speedup without incurring the cost of modifying the entire code. Intelligent process mapping allows differing numbers of processes to be assigned to each domain for load balancing. The revised parallel implementation shows highly promising scalability, obtaining a nearly 80-fold speedup by increasing the number of cores from 30 to 3,335.

fastBMA: scalable network inference and transitive reduction.

PubMed

Hung, Ling-Hong; Shi, Kaiyuan; Wu, Migao; Young, William Chad; Raftery, Adrian E; Yeung, Ka Yee

2017-10-01

Inferring genetic networks from genome-wide expression data is extremely demanding computationally. We have developed fastBMA, a distributed, parallel, and scalable implementation of Bayesian model averaging (BMA) for this purpose. fastBMA also includes a computationally efficient module for eliminating redundant indirect edges in the network by mapping the transitive reduction to an easily solved shortest-path problem. We evaluated the performance of fastBMA on synthetic data and experimental genome-wide time series yeast and human datasets. When using a single CPU core, fastBMA is up to 100 times faster than the next fastest method, LASSO, with increased accuracy. It is a memory-efficient, parallel, and distributed application that scales to human genome-wide expression data. A 10 000-gene regulation network can be obtained in a matter of hours using a 32-core cloud cluster (2 nodes of 16 cores). fastBMA is a significant improvement over its predecessor ScanBMA. It is more accurate and orders of magnitude faster than other fast network inference methods such as the 1 based on LASSO. The improved scalability allows it to calculate networks from genome scale data in a reasonable time frame. The transitive reduction method can improve accuracy in denser networks. fastBMA is available as code (M.I.T. license) from GitHub (https://github.com/lhhunghimself/fastBMA), as part of the updated networkBMA Bioconductor package (https://www.bioconductor.org/packages/release/bioc/html/networkBMA.html) and as ready-to-deploy Docker images (https://hub.docker.com/r/biodepot/fastbma/). © The Authors 2017. Published by Oxford University Press.

3D Kirchhoff depth migration algorithm: A new scalable approach for parallelization on multicore CPU based cluster

NASA Astrophysics Data System (ADS)

Rastogi, Richa; Londhe, Ashutosh; Srivastava, Abhishek; Sirasala, Kirannmayi M.; Khonde, Kiran

2017-03-01

In this article, a new scalable 3D Kirchhoff depth migration algorithm is presented on state of the art multicore CPU based cluster. Parallelization of 3D Kirchhoff depth migration is challenging due to its high demand of compute time, memory, storage and I/O along with the need of their effective management. The most resource intensive modules of the algorithm are traveltime calculations and migration summation which exhibit an inherent trade off between compute time and other resources. The parallelization strategy of the algorithm largely depends on the storage of calculated traveltimes and its feeding mechanism to the migration process. The presented work is an extension of our previous work, wherein a 3D Kirchhoff depth migration application for multicore CPU based parallel system had been developed. Recently, we have worked on improving parallel performance of this application by re-designing the parallelization approach. The new algorithm is capable to efficiently migrate both prestack and poststack 3D data. It exhibits flexibility for migrating large number of traces within the available node memory and with minimal requirement of storage, I/O and inter-node communication. The resultant application is tested using 3D Overthrust data on PARAM Yuva II, which is a Xeon E5-2670 based multicore CPU cluster with 16 cores/node and 64 GB shared memory. Parallel performance of the algorithm is studied using different numerical experiments and the scalability results show striking improvement over its previous version. An impressive 49.05X speedup with 76.64% efficiency is achieved for 3D prestack data and 32.00X speedup with 50.00% efficiency for 3D poststack data, using 64 nodes. The results also demonstrate the effectiveness and robustness of the improved algorithm with high scalability and efficiency on a multicore CPU cluster.

A Multibody Formulation for Three Dimensional Brick Finite Element Based Parallel and Scalable Rotor Dynamic Analysis

DTIC Science & Technology

2010-05-01

connections near the hub end, and containing up to 0.48 million degrees of freedom. The models are analyzed for scala - bility and timing for hover and...Parallel and Scalable Rotor Dynamic Analysis 5a. CONTRACT NUMBER 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR(S) 5d. PROJECT NUMBER 5e. TASK...will enable the modeling of critical couplings that occur in hingeless and bearingless hubs with advanced flex structures. Second , it will enable the

GLAD: a system for developing and deploying large-scale bioinformatics grid.

PubMed

Teo, Yong-Meng; Wang, Xianbing; Ng, Yew-Kwong

2005-03-01

Grid computing is used to solve large-scale bioinformatics problems with gigabytes database by distributing the computation across multiple platforms. Until now in developing bioinformatics grid applications, it is extremely tedious to design and implement the component algorithms and parallelization techniques for different classes of problems, and to access remotely located sequence database files of varying formats across the grid. In this study, we propose a grid programming toolkit, GLAD (Grid Life sciences Applications Developer), which facilitates the development and deployment of bioinformatics applications on a grid. GLAD has been developed using ALiCE (Adaptive scaLable Internet-based Computing Engine), a Java-based grid middleware, which exploits the task-based parallelism. Two bioinformatics benchmark applications, such as distributed sequence comparison and distributed progressive multiple sequence alignment, have been developed using GLAD.

WESTPA: An interoperable, highly scalable software package for weighted ensemble simulation and analysis

PubMed Central

Zwier, Matthew C.; Adelman, Joshua L.; Kaus, Joseph W.; Pratt, Adam J.; Wong, Kim F.; Rego, Nicholas B.; Suárez, Ernesto; Lettieri, Steven; Wang, David W.; Grabe, Michael; Zuckerman, Daniel M.; Chong, Lillian T.

2015-01-01

The weighted ensemble (WE) path sampling approach orchestrates an ensemble of parallel calculations with intermittent communication to enhance the sampling of rare events, such as molecular associations or conformational changes in proteins or peptides. Trajectories are replicated and pruned in a way that focuses computational effort on under-explored regions of configuration space while maintaining rigorous kinetics. To enable the simulation of rare events at any scale (e.g. atomistic, cellular), we have developed an open-source, interoperable, and highly scalable software package for the execution and analysis of WE simulations: WESTPA (The Weighted Ensemble Simulation Toolkit with Parallelization and Analysis). WESTPA scales to thousands of CPU cores and includes a suite of analysis tools that have been implemented in a massively parallel fashion. The software has been designed to interface conveniently with any dynamics engine and has already been used with a variety of molecular dynamics (e.g. GROMACS, NAMD, OpenMM, AMBER) and cell-modeling packages (e.g. BioNetGen, MCell). WESTPA has been in production use for over a year, and its utility has been demonstrated for a broad set of problems, ranging from atomically detailed host-guest associations to non-spatial chemical kinetics of cellular signaling networks. The following describes the design and features of WESTPA, including the facilities it provides for running WE simulations, storing and analyzing WE simulation data, as well as examples of input and output. PMID:26392815

Comparing Neuromorphic Solutions in Action: Implementing a Bio-Inspired Solution to a Benchmark Classification Task on Three Parallel-Computing Platforms

PubMed Central

Diamond, Alan; Nowotny, Thomas; Schmuker, Michael

2016-01-01

Neuromorphic computing employs models of neuronal circuits to solve computing problems. Neuromorphic hardware systems are now becoming more widely available and “neuromorphic algorithms” are being developed. As they are maturing toward deployment in general research environments, it becomes important to assess and compare them in the context of the applications they are meant to solve. This should encompass not just task performance, but also ease of implementation, speed of processing, scalability, and power efficiency. Here, we report our practical experience of implementing a bio-inspired, spiking network for multivariate classification on three different platforms: the hybrid digital/analog Spikey system, the digital spike-based SpiNNaker system, and GeNN, a meta-compiler for parallel GPU hardware. We assess performance using a standard hand-written digit classification task. We found that whilst a different implementation approach was required for each platform, classification performances remained in line. This suggests that all three implementations were able to exercise the model's ability to solve the task rather than exposing inherent platform limits, although differences emerged when capacity was approached. With respect to execution speed and power consumption, we found that for each platform a large fraction of the computing time was spent outside of the neuromorphic device, on the host machine. Time was spent in a range of combinations of preparing the model, encoding suitable input spiking data, shifting data, and decoding spike-encoded results. This is also where a large proportion of the total power was consumed, most markedly for the SpiNNaker and Spikey systems. We conclude that the simulation efficiency advantage of the assessed specialized hardware systems is easily lost in excessive host-device communication, or non-neuronal parts of the computation. These results emphasize the need to optimize the host-device communication architecture for scalability, maximum throughput, and minimum latency. Moreover, our results indicate that special attention should be paid to minimize host-device communication when designing and implementing networks for efficient neuromorphic computing. PMID:26778950

Hybrid-optimization strategy for the communication of large-scale Kinetic Monte Carlo simulation

NASA Astrophysics Data System (ADS)

Wu, Baodong; Li, Shigang; Zhang, Yunquan; Nie, Ningming

2017-02-01

The parallel Kinetic Monte Carlo (KMC) algorithm based on domain decomposition has been widely used in large-scale physical simulations. However, the communication overhead of the parallel KMC algorithm is critical, and severely degrades the overall performance and scalability. In this paper, we present a hybrid optimization strategy to reduce the communication overhead for the parallel KMC simulations. We first propose a communication aggregation algorithm to reduce the total number of messages and eliminate the communication redundancy. Then, we utilize the shared memory to reduce the memory copy overhead of the intra-node communication. Finally, we optimize the communication scheduling using the neighborhood collective operations. We demonstrate the scalability and high performance of our hybrid optimization strategy by both theoretical and experimental analysis. Results show that the optimized KMC algorithm exhibits better performance and scalability than the well-known open-source library-SPPARKS. On 32-node Xeon E5-2680 cluster (total 640 cores), the optimized algorithm reduces the communication time by 24.8% compared with SPPARKS.

Tinker-HP: a massively parallel molecular dynamics package for multiscale simulations of large complex systems with advanced point dipole polarizable force fields† †Electronic supplementary information (ESI) available. See DOI: 10.1039/c7sc04531j

PubMed Central

Lagardère, Louis; Jolly, Luc-Henri; Lipparini, Filippo; Aviat, Félix; Stamm, Benjamin; Jing, Zhifeng F.; Harger, Matthew; Torabifard, Hedieh; Cisneros, G. Andrés; Schnieders, Michael J.; Gresh, Nohad; Maday, Yvon; Ren, Pengyu Y.; Ponder, Jay W.

2017-01-01

We present Tinker-HP, a massively MPI parallel package dedicated to classical molecular dynamics (MD) and to multiscale simulations, using advanced polarizable force fields (PFF) encompassing distributed multipoles electrostatics. Tinker-HP is an evolution of the popular Tinker package code that conserves its simplicity of use and its reference double precision implementation for CPUs. Grounded on interdisciplinary efforts with applied mathematics, Tinker-HP allows for long polarizable MD simulations on large systems up to millions of atoms. We detail in the paper the newly developed extension of massively parallel 3D spatial decomposition to point dipole polarizable models as well as their coupling to efficient Krylov iterative and non-iterative polarization solvers. The design of the code allows the use of various computer systems ranging from laboratory workstations to modern petascale supercomputers with thousands of cores. Tinker-HP proposes therefore the first high-performance scalable CPU computing environment for the development of next generation point dipole PFFs and for production simulations. Strategies linking Tinker-HP to Quantum Mechanics (QM) in the framework of multiscale polarizable self-consistent QM/MD simulations are also provided. The possibilities, performances and scalability of the software are demonstrated via benchmarks calculations using the polarizable AMOEBA force field on systems ranging from large water boxes of increasing size and ionic liquids to (very) large biosystems encompassing several proteins as well as the complete satellite tobacco mosaic virus and ribosome structures. For small systems, Tinker-HP appears to be competitive with the Tinker-OpenMM GPU implementation of Tinker. As the system size grows, Tinker-HP remains operational thanks to its access to distributed memory and takes advantage of its new algorithmic enabling for stable long timescale polarizable simulations. Overall, a several thousand-fold acceleration over a single-core computation is observed for the largest systems. The extension of the present CPU implementation of Tinker-HP to other computational platforms is discussed. PMID:29732110

An efficient implementation of a high-order filter for a cubed-sphere spectral element model

NASA Astrophysics Data System (ADS)

Kang, Hyun-Gyu; Cheong, Hyeong-Bin

2017-03-01

A parallel-scalable, isotropic, scale-selective spatial filter was developed for the cubed-sphere spectral element model on the sphere. The filter equation is a high-order elliptic (Helmholtz) equation based on the spherical Laplacian operator, which is transformed into cubed-sphere local coordinates. The Laplacian operator is discretized on the computational domain, i.e., on each cell, by the spectral element method with Gauss-Lobatto Lagrange interpolating polynomials (GLLIPs) as the orthogonal basis functions. On the global domain, the discrete filter equation yielded a linear system represented by a highly sparse matrix. The density of this matrix increases quadratically (linearly) with the order of GLLIP (order of the filter), and the linear system is solved in only O (Ng) operations, where Ng is the total number of grid points. The solution, obtained by a row reduction method, demonstrated the typical accuracy and convergence rate of the cubed-sphere spectral element method. To achieve computational efficiency on parallel computers, the linear system was treated by an inverse matrix method (a sparse matrix-vector multiplication). The density of the inverse matrix was lowered to only a few times of the original sparse matrix without degrading the accuracy of the solution. For better computational efficiency, a local-domain high-order filter was introduced: The filter equation is applied to multiple cells, and then the central cell was only used to reconstruct the filtered field. The parallel efficiency of applying the inverse matrix method to the global- and local-domain filter was evaluated by the scalability on a distributed-memory parallel computer. The scale-selective performance of the filter was demonstrated on Earth topography. The usefulness of the filter as a hyper-viscosity for the vorticity equation was also demonstrated.

Many-integrated core (MIC) technology for accelerating Monte Carlo simulation of radiation transport: A study based on the code DPM

NASA Astrophysics Data System (ADS)

Rodriguez, M.; Brualla, L.

2018-04-01

Monte Carlo simulation of radiation transport is computationally demanding to obtain reasonably low statistical uncertainties of the estimated quantities. Therefore, it can benefit in a large extent from high-performance computing. This work is aimed at assessing the performance of the first generation of the many-integrated core architecture (MIC) Xeon Phi coprocessor with respect to that of a CPU consisting of a double 12-core Xeon processor in Monte Carlo simulation of coupled electron-photonshowers. The comparison was made twofold, first, through a suite of basic tests including parallel versions of the random number generators Mersenne Twister and a modified implementation of RANECU. These tests were addressed to establish a baseline comparison between both devices. Secondly, through the p DPM code developed in this work. p DPM is a parallel version of the Dose Planning Method (DPM) program for fast Monte Carlo simulation of radiation transport in voxelized geometries. A variety of techniques addressed to obtain a large scalability on the Xeon Phi were implemented in p DPM. Maximum scalabilities of 84 . 2 × and 107 . 5 × were obtained in the Xeon Phi for simulations of electron and photon beams, respectively. Nevertheless, in none of the tests involving radiation transport the Xeon Phi performed better than the CPU. The disadvantage of the Xeon Phi with respect to the CPU owes to the low performance of the single core of the former. A single core of the Xeon Phi was more than 10 times less efficient than a single core of the CPU for all radiation transport simulations.

The Computational Complexity, Parallel Scalability, and Performance of Atmospheric Data Assimilation Algorithms

NASA Technical Reports Server (NTRS)

Lyster, Peter M.; Guo, J.; Clune, T.; Larson, J. W.; Atlas, Robert (Technical Monitor)

2001-01-01

The computational complexity of algorithms for Four Dimensional Data Assimilation (4DDA) at NASA's Data Assimilation Office (DAO) is discussed. In 4DDA, observations are assimilated with the output of a dynamical model to generate best-estimates of the states of the system. It is thus a mapping problem, whereby scattered observations are converted into regular accurate maps of wind, temperature, moisture and other variables. The DAO is developing and using 4DDA algorithms that provide these datasets, or analyses, in support of Earth System Science research. Two large-scale algorithms are discussed. The first approach, the Goddard Earth Observing System Data Assimilation System (GEOS DAS), uses an atmospheric general circulation model (GCM) and an observation-space based analysis system, the Physical-space Statistical Analysis System (PSAS). GEOS DAS is very similar to global meteorological weather forecasting data assimilation systems, but is used at NASA for climate research. Systems of this size typically run at between 1 and 20 gigaflop/s. The second approach, the Kalman filter, uses a more consistent algorithm to determine the forecast error covariance matrix than does GEOS DAS. For atmospheric assimilation, the gridded dynamical fields typically have More than 10(exp 6) variables, therefore the full error covariance matrix may be in excess of a teraword. For the Kalman filter this problem can easily scale to petaflop/s proportions. We discuss the computational complexity of GEOS DAS and our implementation of the Kalman filter. We also discuss and quantify some of the technical issues and limitations in developing efficient, in terms of wall clock time, and scalable parallel implementations of the algorithms.

A Multi-Time Scale Morphable Software Milieu for Polymorphous Computing Architectures (PCA) - Composable, Scalable Systems

DTIC Science & Technology

2004-10-01

MONITORING AGENCY NAME(S) AND ADDRESS(ES) Defense Advanced Research Projects Agency AFRL/IFTC 3701 North Fairfax Drive...Scalable Parallel Libraries for Large-Scale Concurrent Applications," Technical Report UCRL -JC-109251, Lawrence Livermore National Laboratory

OligoIS: Scalable Instance Selection for Class-Imbalanced Data Sets.

PubMed

García-Pedrajas, Nicolás; Perez-Rodríguez, Javier; de Haro-García, Aida

2013-02-01

In current research, an enormous amount of information is constantly being produced, which poses a challenge for data mining algorithms. Many of the problems in extremely active research areas, such as bioinformatics, security and intrusion detection, or text mining, share the following two features: large data sets and class-imbalanced distribution of samples. Although many methods have been proposed for dealing with class-imbalanced data sets, most of these methods are not scalable to the very large data sets common to those research fields. In this paper, we propose a new approach to dealing with the class-imbalance problem that is scalable to data sets with many millions of instances and hundreds of features. This proposal is based on the divide-and-conquer principle combined with application of the selection process to balanced subsets of the whole data set. This divide-and-conquer principle allows the execution of the algorithm in linear time. Furthermore, the proposed method is easy to implement using a parallel environment and can work without loading the whole data set into memory. Using 40 class-imbalanced medium-sized data sets, we will demonstrate our method's ability to improve the results of state-of-the-art instance selection methods for class-imbalanced data sets. Using three very large data sets, we will show the scalability of our proposal to millions of instances and hundreds of features.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ibrahim, Khaled Z.; Epifanovsky, Evgeny; Williams, Samuel W.

Coupled-cluster methods provide highly accurate models of molecular structure by explicit numerical calculation of tensors representing the correlation between electrons. These calculations are dominated by a sequence of tensor contractions, motivating the development of numerical libraries for such operations. While based on matrix-matrix multiplication, these libraries are specialized to exploit symmetries in the molecular structure and in electronic interactions, and thus reduce the size of the tensor representation and the complexity of contractions. The resulting algorithms are irregular and their parallelization has been previously achieved via the use of dynamic scheduling or specialized data decompositions. We introduce our efforts tomore » extend the Libtensor framework to work in the distributed memory environment in a scalable and energy efficient manner. We achieve up to 240 speedup compared with the best optimized shared memory implementation. We attain scalability to hundreds of thousands of compute cores on three distributed-memory architectures, (Cray XC30&XC40, BlueGene/Q), and on a heterogeneous GPU-CPU system (Cray XK7). As the bottlenecks shift from being compute-bound DGEMM's to communication-bound collectives as the size of the molecular system scales, we adopt two radically different parallelization approaches for handling load-imbalance. Nevertheless, we preserve a uni ed interface to both programming models to maintain the productivity of computational quantum chemists.« less

Scalable, High-performance 3D Imaging Software Platform: System Architecture and Application to Virtual Colonoscopy

PubMed Central

Yoshida, Hiroyuki; Wu, Yin; Cai, Wenli; Brett, Bevin

2013-01-01

One of the key challenges in three-dimensional (3D) medical imaging is to enable the fast turn-around time, which is often required for interactive or real-time response. This inevitably requires not only high computational power but also high memory bandwidth due to the massive amount of data that need to be processed. In this work, we have developed a software platform that is designed to support high-performance 3D medical image processing for a wide range of applications using increasingly available and affordable commodity computing systems: multi-core, clusters, and cloud computing systems. To achieve scalable, high-performance computing, our platform (1) employs size-adaptive, distributable block volumes as a core data structure for efficient parallelization of a wide range of 3D image processing algorithms; (2) supports task scheduling for efficient load distribution and balancing; and (3) consists of a layered parallel software libraries that allow a wide range of medical applications to share the same functionalities. We evaluated the performance of our platform by applying it to an electronic cleansing system in virtual colonoscopy, with initial experimental results showing a 10 times performance improvement on an 8-core workstation over the original sequential implementation of the system. PMID:23366803

Scalable Metropolis Monte Carlo for simulation of hard shapes

NASA Astrophysics Data System (ADS)

Anderson, Joshua A.; Eric Irrgang, M.; Glotzer, Sharon C.

2016-07-01

We design and implement a scalable hard particle Monte Carlo simulation toolkit (HPMC), and release it open source as part of HOOMD-blue. HPMC runs in parallel on many CPUs and many GPUs using domain decomposition. We employ BVH trees instead of cell lists on the CPU for fast performance, especially with large particle size disparity, and optimize inner loops with SIMD vector intrinsics on the CPU. Our GPU kernel proposes many trial moves in parallel on a checkerboard and uses a block-level queue to redistribute work among threads and avoid divergence. HPMC supports a wide variety of shape classes, including spheres/disks, unions of spheres, convex polygons, convex spheropolygons, concave polygons, ellipsoids/ellipses, convex polyhedra, convex spheropolyhedra, spheres cut by planes, and concave polyhedra. NVT and NPT ensembles can be run in 2D or 3D triclinic boxes. Additional integration schemes permit Frenkel-Ladd free energy computations and implicit depletant simulations. In a benchmark system of a fluid of 4096 pentagons, HPMC performs 10 million sweeps in 10 min on 96 CPU cores on XSEDE Comet. The same simulation would take 7.6 h in serial. HPMC also scales to large system sizes, and the same benchmark with 16.8 million particles runs in 1.4 h on 2048 GPUs on OLCF Titan.

Scalable Cloning on Large-Scale GPU Platforms with Application to Time-Stepped Simulations on Grids

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yoginath, Srikanth B.; Perumalla, Kalyan S.

Cloning is a technique to efficiently simulate a tree of multiple what-if scenarios that are unraveled during the course of a base simulation. However, cloned execution is highly challenging to realize on large, distributed memory computing platforms, due to the dynamic nature of the computational load across clones, and due to the complex dependencies spanning the clone tree. In this paper, we present the conceptual simulation framework, algorithmic foundations, and runtime interface of CloneX, a new system we designed for scalable simulation cloning. It efficiently and dynamically creates whole logical copies of a dynamic tree of simulations across a largemore » parallel system without full physical duplication of computation and memory. The performance of a prototype implementation executed on up to 1,024 graphical processing units of a supercomputing system has been evaluated with three benchmarks—heat diffusion, forest fire, and disease propagation models—delivering a speed up of over two orders of magnitude compared to replicated runs. Finally, the results demonstrate a significantly faster and scalable way to execute many what-if scenario ensembles of large simulations via cloning using the CloneX interface.« less

A scalable nonlinear fluid-structure interaction solver based on a Schwarz preconditioner with isogeometric unstructured coarse spaces in 3D

NASA Astrophysics Data System (ADS)

Kong, Fande; Cai, Xiao-Chuan

2017-07-01

Nonlinear fluid-structure interaction (FSI) problems on unstructured meshes in 3D appear in many applications in science and engineering, such as vibration analysis of aircrafts and patient-specific diagnosis of cardiovascular diseases. In this work, we develop a highly scalable, parallel algorithmic and software framework for FSI problems consisting of a nonlinear fluid system and a nonlinear solid system, that are coupled monolithically. The FSI system is discretized by a stabilized finite element method in space and a fully implicit backward difference scheme in time. To solve the large, sparse system of nonlinear algebraic equations at each time step, we propose an inexact Newton-Krylov method together with a multilevel, smoothed Schwarz preconditioner with isogeometric coarse meshes generated by a geometry preserving coarsening algorithm. Here "geometry" includes the boundary of the computational domain and the wet interface between the fluid and the solid. We show numerically that the proposed algorithm and implementation are highly scalable in terms of the number of linear and nonlinear iterations and the total compute time on a supercomputer with more than 10,000 processor cores for several problems with hundreds of millions of unknowns.

A scalable nonlinear fluid–structure interaction solver based on a Schwarz preconditioner with isogeometric unstructured coarse spaces in 3D

DOE PAGES

Kong, Fande; Cai, Xiao-Chuan

2017-03-24

Nonlinear fluid-structure interaction (FSI) problems on unstructured meshes in 3D appear many applications in science and engineering, such as vibration analysis of aircrafts and patient-specific diagnosis of cardiovascular diseases. In this work, we develop a highly scalable, parallel algorithmic and software framework for FSI problems consisting of a nonlinear fluid system and a nonlinear solid system, that are coupled monolithically. The FSI system is discretized by a stabilized finite element method in space and a fully implicit backward difference scheme in time. To solve the large, sparse system of nonlinear algebraic equations at each time step, we propose an inexactmore » Newton-Krylov method together with a multilevel, smoothed Schwarz preconditioner with isogeometric coarse meshes generated by a geometry preserving coarsening algorithm. Here ''geometry'' includes the boundary of the computational domain and the wet interface between the fluid and the solid. We show numerically that the proposed algorithm and implementation are highly scalable in terms of the number of linear and nonlinear iterations and the total compute time on a supercomputer with more than 10,000 processor cores for several problems with hundreds of millions of unknowns.« less

Scalable Cloning on Large-Scale GPU Platforms with Application to Time-Stepped Simulations on Grids

DOE PAGES

Yoginath, Srikanth B.; Perumalla, Kalyan S.

2018-01-31

Cloning is a technique to efficiently simulate a tree of multiple what-if scenarios that are unraveled during the course of a base simulation. However, cloned execution is highly challenging to realize on large, distributed memory computing platforms, due to the dynamic nature of the computational load across clones, and due to the complex dependencies spanning the clone tree. In this paper, we present the conceptual simulation framework, algorithmic foundations, and runtime interface of CloneX, a new system we designed for scalable simulation cloning. It efficiently and dynamically creates whole logical copies of a dynamic tree of simulations across a largemore » parallel system without full physical duplication of computation and memory. The performance of a prototype implementation executed on up to 1,024 graphical processing units of a supercomputing system has been evaluated with three benchmarks—heat diffusion, forest fire, and disease propagation models—delivering a speed up of over two orders of magnitude compared to replicated runs. Finally, the results demonstrate a significantly faster and scalable way to execute many what-if scenario ensembles of large simulations via cloning using the CloneX interface.« less

A Fine-Grained Pipelined Implementation for Large-Scale Matrix Inversion on FPGA

NASA Astrophysics Data System (ADS)

Zhou, Jie; Dou, Yong; Zhao, Jianxun; Xia, Fei; Lei, Yuanwu; Tang, Yuxing

Large-scale matrix inversion play an important role in many applications. However to the best of our knowledge, there is no FPGA-based implementation. In this paper, we explore the possibility of accelerating large-scale matrix inversion on FPGA. To exploit the computational potential of FPGA, we introduce a fine-grained parallel algorithm for matrix inversion. A scalable linear array processing elements (PEs), which is the core component of the FPGA accelerator, is proposed to implement this algorithm. A total of 12 PEs can be integrated into an Altera StratixII EP2S130F1020C5 FPGA on our self-designed board. Experimental results show that a factor of 2.6 speedup and the maximum power-performance of 41 can be achieved compare to Pentium Dual CPU with double SSE threads.

Scalable Static and Dynamic Community Detection Using Grappolo

DOE Office of Scientific and Technical Information (OSTI.GOV)

Halappanavar, Mahantesh; Lu, Hao; Kalyanaraman, Anantharaman

Graph clustering, popularly known as community detection, is a fundamental kernel for several applications of relevance to the Defense Advanced Research Projects Agency’s (DARPA) Hierarchical Identify Verify Exploit (HIVE) Pro- gram. Clusters or communities represent natural divisions within a network that are densely connected within a cluster and sparsely connected to the rest of the network. The need to compute clustering on large scale data necessitates the development of efficient algorithms that can exploit modern architectures that are fundamentally parallel in nature. How- ever, due to their irregular and inherently sequential nature, many of the current algorithms for community detectionmore » are challenging to parallelize. In response to the HIVE Graph Challenge, we present several parallelization heuristics for fast community detection using the Louvain method as the serial template. We implement all the heuristics in a software library called Grappolo. Using the inputs from the HIVE Challenge, we demonstrate superior performance and high quality solutions based on four parallelization heuristics. We use Grappolo on static graphs as the first step towards community detection on streaming graphs.« less

On Parallelizing Single Dynamic Simulation Using HPC Techniques and APIs of Commercial Software

DOE Office of Scientific and Technical Information (OSTI.GOV)

Diao, Ruisheng; Jin, Shuangshuang; Howell, Frederic

Time-domain simulations are heavily used in today’s planning and operation practices to assess power system transient stability and post-transient voltage/frequency profiles following severe contingencies to comply with industry standards. Because of the increased modeling complexity, it is several times slower than real time for state-of-the-art commercial packages to complete a dynamic simulation for a large-scale model. With the growing stochastic behavior introduced by emerging technologies, power industry has seen a growing need for performing security assessment in real time. This paper presents a parallel implementation framework to speed up a single dynamic simulation by leveraging the existing stability model librarymore » in commercial tools through their application programming interfaces (APIs). Several high performance computing (HPC) techniques are explored such as parallelizing the calculation of generator current injection, identifying fast linear solvers for network solution, and parallelizing data outputs when interacting with APIs in the commercial package, TSAT. The proposed method has been tested on a WECC planning base case with detailed synchronous generator models and exhibits outstanding scalable performance with sufficient accuracy.« less

A Numerical Study of Scalable Cardiac Electro-Mechanical Solvers on HPC Architectures

PubMed Central

Colli Franzone, Piero; Pavarino, Luca F.; Scacchi, Simone

2018-01-01

We introduce and study some scalable domain decomposition preconditioners for cardiac electro-mechanical 3D simulations on parallel HPC (High Performance Computing) architectures. The electro-mechanical model of the cardiac tissue is composed of four coupled sub-models: (1) the static finite elasticity equations for the transversely isotropic deformation of the cardiac tissue; (2) the active tension model describing the dynamics of the intracellular calcium, cross-bridge binding and myofilament tension; (3) the anisotropic Bidomain model describing the evolution of the intra- and extra-cellular potentials in the deforming cardiac tissue; and (4) the ionic membrane model describing the dynamics of ionic currents, gating variables, ionic concentrations and stretch-activated channels. This strongly coupled electro-mechanical model is discretized in time with a splitting semi-implicit technique and in space with isoparametric finite elements. The resulting scalable parallel solver is based on Multilevel Additive Schwarz preconditioners for the solution of the Bidomain system and on BDDC preconditioned Newton-Krylov solvers for the non-linear finite elasticity system. The results of several 3D parallel simulations show the scalability of both linear and non-linear solvers and their application to the study of both physiological excitation-contraction cardiac dynamics and re-entrant waves in the presence of different mechano-electrical feedbacks. PMID:29674971

Large-Scale Parallel Viscous Flow Computations using an Unstructured Multigrid Algorithm

NASA Technical Reports Server (NTRS)

Mavriplis, Dimitri J.

1999-01-01

The development and testing of a parallel unstructured agglomeration multigrid algorithm for steady-state aerodynamic flows is discussed. The agglomeration multigrid strategy uses a graph algorithm to construct the coarse multigrid levels from the given fine grid, similar to an algebraic multigrid approach, but operates directly on the non-linear system using the FAS (Full Approximation Scheme) approach. The scalability and convergence rate of the multigrid algorithm are examined on the SGI Origin 2000 and the Cray T3E. An argument is given which indicates that the asymptotic scalability of the multigrid algorithm should be similar to that of its underlying single grid smoothing scheme. For medium size problems involving several million grid points, near perfect scalability is obtained for the single grid algorithm, while only a slight drop-off in parallel efficiency is observed for the multigrid V- and W-cycles, using up to 128 processors on the SGI Origin 2000, and up to 512 processors on the Cray T3E. For a large problem using 25 million grid points, good scalability is observed for the multigrid algorithm using up to 1450 processors on a Cray T3E, even when the coarsest grid level contains fewer points than the total number of processors.

SciSpark: Highly Interactive and Scalable Model Evaluation and Climate Metrics

NASA Astrophysics Data System (ADS)

Wilson, B. D.; Palamuttam, R. S.; Mogrovejo, R. M.; Whitehall, K. D.; Mattmann, C. A.; Verma, R.; Waliser, D. E.; Lee, H.

2015-12-01

Remote sensing data and climate model output are multi-dimensional arrays of massive sizes locked away in heterogeneous file formats (HDF5/4, NetCDF 3/4) and metadata models (HDF-EOS, CF) making it difficult to perform multi-stage, iterative science processing since each stage requires writing and reading data to and from disk. We are developing a lightning fast Big Data technology called SciSpark based on ApacheTM Spark under a NASA AIST grant (PI Mattmann). Spark implements the map-reduce paradigm for parallel computing on a cluster, but emphasizes in-memory computation, "spilling" to disk only as needed, and so outperforms the disk-based ApacheTM Hadoop by 100x in memory and by 10x on disk. SciSpark will enable scalable model evaluation by executing large-scale comparisons of A-Train satellite observations to model grids on a cluster of 10 to 1000 compute nodes. This 2nd generation capability for NASA's Regional Climate Model Evaluation System (RCMES) will compute simple climate metrics at interactive speeds, and extend to quite sophisticated iterative algorithms such as machine-learning based clustering of temperature PDFs, and even graph-based algorithms for searching for Mesocale Convective Complexes. We have implemented a parallel data ingest capability in which the user specifies desired variables (arrays) as several time-sorted lists of URL's (i.e. using OPeNDAP model.nc?varname, or local files). The specified variables are partitioned by time/space and then each Spark node pulls its bundle of arrays into memory to begin a computation pipeline. We also investigated the performance of several N-dim. array libraries (scala breeze, java jblas & netlib-java, and ND4J). We are currently developing science codes using ND4J and studying memory behavior on the JVM. On the pyspark side, many of our science codes already use the numpy and SciPy ecosystems. The talk will cover: the architecture of SciSpark, the design of the scientific RDD (sRDD) data structure, our efforts to integrate climate science algorithms in Python and Scala, parallel ingest and partitioning of A-Train satellite observations from HDF files and model grids from netCDF files, first parallel runs to compute comparison statistics and PDF's, and first metrics quantifying parallel speedups and memory & disk usage.

A Divergence Statistics Extension to VTK for Performance Analysis

DOE Office of Scientific and Technical Information (OSTI.GOV)

Pebay, Philippe Pierre; Bennett, Janine Camille

This report follows the series of previous documents ([PT08, BPRT09b, PT09, BPT09, PT10, PB13], where we presented the parallel descriptive, correlative, multi-correlative, principal component analysis, contingency, k -means, order and auto-correlative statistics engines which we developed within the Visualization Tool Kit ( VTK ) as a scalable, parallel and versatile statistics package. We now report on a new engine which we developed for the calculation of divergence statistics, a concept which we hereafter explain and whose main goal is to quantify the discrepancy, in a stasticial manner akin to measuring a distance, between an observed empirical distribution and a theoretical,more » "ideal" one. The ease of use of the new diverence statistics engine is illustrated by the means of C++ code snippets. Although this new engine does not yet have a parallel implementation, it has already been applied to HPC performance analysis, of which we provide an example.« less

GROMACS 4:  Algorithms for Highly Efficient, Load-Balanced, and Scalable Molecular Simulation.

PubMed

Hess, Berk; Kutzner, Carsten; van der Spoel, David; Lindahl, Erik

2008-03-01

Molecular simulation is an extremely useful, but computationally very expensive tool for studies of chemical and biomolecular systems. Here, we present a new implementation of our molecular simulation toolkit GROMACS which now both achieves extremely high performance on single processors from algorithmic optimizations and hand-coded routines and simultaneously scales very well on parallel machines. The code encompasses a minimal-communication domain decomposition algorithm, full dynamic load balancing, a state-of-the-art parallel constraint solver, and efficient virtual site algorithms that allow removal of hydrogen atom degrees of freedom to enable integration time steps up to 5 fs for atomistic simulations also in parallel. To improve the scaling properties of the common particle mesh Ewald electrostatics algorithms, we have in addition used a Multiple-Program, Multiple-Data approach, with separate node domains responsible for direct and reciprocal space interactions. Not only does this combination of algorithms enable extremely long simulations of large systems but also it provides that simulation performance on quite modest numbers of standard cluster nodes.

Parallel Navier-Stokes computations on shared and distributed memory architectures

NASA Technical Reports Server (NTRS)

Hayder, M. Ehtesham; Jayasimha, D. N.; Pillay, Sasi Kumar

1995-01-01

We study a high order finite difference scheme to solve the time accurate flow field of a jet using the compressible Navier-Stokes equations. As part of our ongoing efforts, we have implemented our numerical model on three parallel computing platforms to study the computational, communication, and scalability characteristics. The platforms chosen for this study are a cluster of workstations connected through fast networks (the LACE experimental testbed at NASA Lewis), a shared memory multiprocessor (the Cray YMP), and a distributed memory multiprocessor (the IBM SPI). Our focus in this study is on the LACE testbed. We present some results for the Cray YMP and the IBM SP1 mainly for comparison purposes. On the LACE testbed, we study: (1) the communication characteristics of Ethernet, FDDI, and the ALLNODE networks and (2) the overheads induced by the PVM message passing library used for parallelizing the application. We demonstrate that clustering of workstations is effective and has the potential to be computationally competitive with supercomputers at a fraction of the cost.

THC-MP: High performance numerical simulation of reactive transport and multiphase flow in porous media

NASA Astrophysics Data System (ADS)

Wei, Xiaohui; Li, Weishan; Tian, Hailong; Li, Hongliang; Xu, Haixiao; Xu, Tianfu

2015-07-01

The numerical simulation of multiphase flow and reactive transport in the porous media on complex subsurface problem is a computationally intensive application. To meet the increasingly computational requirements, this paper presents a parallel computing method and architecture. Derived from TOUGHREACT that is a well-established code for simulating subsurface multi-phase flow and reactive transport problems, we developed a high performance computing THC-MP based on massive parallel computer, which extends greatly on the computational capability for the original code. The domain decomposition method was applied to the coupled numerical computing procedure in the THC-MP. We designed the distributed data structure, implemented the data initialization and exchange between the computing nodes and the core solving module using the hybrid parallel iterative and direct solver. Numerical accuracy of the THC-MP was verified through a CO2 injection-induced reactive transport problem by comparing the results obtained from the parallel computing and sequential computing (original code). Execution efficiency and code scalability were examined through field scale carbon sequestration applications on the multicore cluster. The results demonstrate successfully the enhanced performance using the THC-MP on parallel computing facilities.

Parallel distributed, reciprocal Monte Carlo radiation in coupled, large eddy combustion simulations

NASA Astrophysics Data System (ADS)

Hunsaker, Isaac L.

Radiation is the dominant mode of heat transfer in high temperature combustion environments. Radiative heat transfer affects the gas and particle phases, including all the associated combustion chemistry. The radiative properties are in turn affected by the turbulent flow field. This bi-directional coupling of radiation turbulence interactions poses a major challenge in creating parallel-capable, high-fidelity combustion simulations. In this work, a new model was developed in which reciprocal monte carlo radiation was coupled with a turbulent, large-eddy simulation combustion model. A technique wherein domain patches are stitched together was implemented to allow for scalable parallelism. The combustion model runs in parallel on a decomposed domain. The radiation model runs in parallel on a recomposed domain. The recomposed domain is stored on each processor after information sharing of the decomposed domain is handled via the message passing interface. Verification and validation testing of the new radiation model were favorable. Strong scaling analyses were performed on the Ember cluster and the Titan cluster for the CPU-radiation model and GPU-radiation model, respectively. The model demonstrated strong scaling to over 1,700 and 16,000 processing cores on Ember and Titan, respectively.

Multi-Purpose, Application-Centric, Scalable I/O Proxy Application

DOE Office of Scientific and Technical Information (OSTI.GOV)

Miller, M. C.

2015-06-15

MACSio is a Multi-purpose, Application-Centric, Scalable I/O proxy application. It is designed to support a number of goals with respect to parallel I/O performance testing and benchmarking including the ability to test and compare various I/O libraries and I/O paradigms, to predict scalable performance of real applications and to help identify where improvements in I/O performance can be made within the HPC I/O software stack.

A task-based parallelism and vectorized approach to 3D Method of Characteristics (MOC) reactor simulation for high performance computing architectures

NASA Astrophysics Data System (ADS)

Tramm, John R.; Gunow, Geoffrey; He, Tim; Smith, Kord S.; Forget, Benoit; Siegel, Andrew R.

2016-05-01

In this study we present and analyze a formulation of the 3D Method of Characteristics (MOC) technique applied to the simulation of full core nuclear reactors. Key features of the algorithm include a task-based parallelism model that allows independent MOC tracks to be assigned to threads dynamically, ensuring load balancing, and a wide vectorizable inner loop that takes advantage of modern SIMD computer architectures. The algorithm is implemented in a set of highly optimized proxy applications in order to investigate its performance characteristics on CPU, GPU, and Intel Xeon Phi architectures. Speed, power, and hardware cost efficiencies are compared. Additionally, performance bottlenecks are identified for each architecture in order to determine the prospects for continued scalability of the algorithm on next generation HPC architectures.

Generating Billion-Edge Scale-Free Networks in Seconds: Performance Study of a Novel GPU-based Preferential Attachment Model

DOE Office of Scientific and Technical Information (OSTI.GOV)

Perumalla, Kalyan S.; Alam, Maksudul

A novel parallel algorithm is presented for generating random scale-free networks using the preferential-attachment model. The algorithm, named cuPPA, is custom-designed for single instruction multiple data (SIMD) style of parallel processing supported by modern processors such as graphical processing units (GPUs). To the best of our knowledge, our algorithm is the first to exploit GPUs, and also the fastest implementation available today, to generate scale free networks using the preferential attachment model. A detailed performance study is presented to understand the scalability and runtime characteristics of the cuPPA algorithm. In one of the best cases, when executed on an NVidiamore » GeForce 1080 GPU, cuPPA generates a scale free network of a billion edges in less than 2 seconds.« less

Parallel Conjugate Gradient: Effects of Ordering Strategies, Programming Paradigms, and Architectural Platforms

NASA Technical Reports Server (NTRS)

Oliker, Leonid; Heber, Gerd; Biswas, Rupak

2000-01-01

The Conjugate Gradient (CG) algorithm is perhaps the best-known iterative technique to solve sparse linear systems that are symmetric and positive definite. A sparse matrix-vector multiply (SPMV) usually accounts for most of the floating-point operations within a CG iteration. In this paper, we investigate the effects of various ordering and partitioning strategies on the performance of parallel CG and SPMV using different programming paradigms and architectures. Results show that for this class of applications, ordering significantly improves overall performance, that cache reuse may be more important than reducing communication, and that it is possible to achieve message passing performance using shared memory constructs through careful data ordering and distribution. However, a multi-threaded implementation of CG on the Tera MTA does not require special ordering or partitioning to obtain high efficiency and scalability.

Advanced Technology and Mitigation (ATDM) SPARC Re-Entry Code Fiscal Year 2017 Progress and Accomplishments for ECP.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Crozier, Paul; Howard, Micah; Rider, William J.

The SPARC (Sandia Parallel Aerodynamics and Reentry Code) will provide nuclear weapon qualification evidence for the random vibration and thermal environments created by re-entry of a warhead into the earth’s atmosphere. SPARC incorporates the innovative approaches of ATDM projects on several fronts including: effective harnessing of heterogeneous compute nodes using Kokkos, exascale-ready parallel scalability through asynchronous multi-tasking, uncertainty quantification through Sacado integration, implementation of state-of-the-art reentry physics and multiscale models, use of advanced verification and validation methods, and enabling of improved workflows for users. SPARC is being developed primarily for the Department of Energy nuclear weapon program, with additional developmentmore » and use of the code is being supported by the Department of Defense for conventional weapons programs.« less

Field-Programmable Gate Array Computer in Structural Analysis: An Initial Exploration

NASA Technical Reports Server (NTRS)

Singleterry, Robert C., Jr.; Sobieszczanski-Sobieski, Jaroslaw; Brown, Samuel

2002-01-01

This paper reports on an initial assessment of using a Field-Programmable Gate Array (FPGA) computational device as a new tool for solving structural mechanics problems. A FPGA is an assemblage of binary gates arranged in logical blocks that are interconnected via software in a manner dependent on the algorithm being implemented and can be reprogrammed thousands of times per second. In effect, this creates a computer specialized for the problem that automatically exploits all the potential for parallel computing intrinsic in an algorithm. This inherent parallelism is the most important feature of the FPGA computational environment. It is therefore important that if a problem offers a choice of different solution algorithms, an algorithm of a higher degree of inherent parallelism should be selected. It is found that in structural analysis, an 'analog computer' style of programming, which solves problems by direct simulation of the terms in the governing differential equations, yields a more favorable solution algorithm than current solution methods. This style of programming is facilitated by a 'drag-and-drop' graphic programming language that is supplied with the particular type of FPGA computer reported in this paper. Simple examples in structural dynamics and statics illustrate the solution approach used. The FPGA system also allows linear scalability in computing capability. As the problem grows, the number of FPGA chips can be increased with no loss of computing efficiency due to data flow or algorithmic latency that occurs when a single problem is distributed among many conventional processors that operate in parallel. This initial assessment finds the FPGA hardware and software to be in their infancy in regard to the user conveniences; however, they have enormous potential for shrinking the elapsed time of structural analysis solutions if programmed with algorithms that exhibit inherent parallelism and linear scalability. This potential warrants further development of FPGA-tailored algorithms for structural analysis.

Replica-exchange Wang Landau sampling: pushing the limits of Monte Carlo simulations in materials sciences

DOE Office of Scientific and Technical Information (OSTI.GOV)

Perera, Meewanage Dilina N; Li, Ying Wai; Eisenbach, Markus

We describe the study of thermodynamics of materials using replica-exchange Wang Landau (REWL) sampling, a generic framework for massively parallel implementations of the Wang Landau Monte Carlo method. To evaluate the performance and scalability of the method, we investigate the magnetic phase transition in body-centered cubic (bcc) iron using the classical Heisenberg model parameterized with first principles calculations. We demonstrate that our framework leads to a significant speedup without compromising the accuracy and precision and facilitates the study of much larger systems than is possible with its serial counterpart.

PETSc Users Manual Revision 3.3

DOE Office of Scientific and Technical Information (OSTI.GOV)

Balay, S.; Brown, J.; Buschelman, K.

This manual describes the use of PETSc for the numerical solution of partial differential equations and related problems on high-performance computers. The Portable, Extensible Toolkit for Scientific Computation (PETSc) is a suite of data structures and routines that provide the building blocks for the implementation of large-scale application codes on parallel (and serial) computers. PETSc uses the MPI standard for all message-passing communication. PETSc includes an expanding suite of parallel linear, nonlinear equation solvers and time integrators that may be used in application codes written in Fortran, C, C++, Python, and MATLAB (sequential). PETSc provides many of the mechanisms neededmore » within parallel application codes, such as parallel matrix and vector assembly routines. The library is organized hierarchically, enabling users to employ the level of abstraction that is most appropriate for a particular problem. By using techniques of object-oriented programming, PETSc provides enormous flexibility for users. PETSc is a sophisticated set of software tools; as such, for some users it initially has a much steeper learning curve than a simple subroutine library. In particular, for individuals without some computer science background, experience programming in C, C++ or Fortran and experience using a debugger such as gdb or dbx, it may require a significant amount of time to take full advantage of the features that enable efficient software use. However, the power of the PETSc design and the algorithms it incorporates may make the efficient implementation of many application codes simpler than “rolling them” yourself; For many tasks a package such as MATLAB is often the best tool; PETSc is not intended for the classes of problems for which effective MATLAB code can be written. PETSc also has a MATLAB interface, so portions of your code can be written in MATLAB to “try out” the PETSc solvers. The resulting code will not be scalable however because currently MATLAB is inherently not scalable; and PETSc should not be used to attempt to provide a “parallel linear solver” in an otherwise sequential code. Certainly all parts of a previously sequential code need not be parallelized but the matrix generation portion must be parallelized to expect any kind of reasonable performance. Do not expect to generate your matrix sequentially and then “use PETSc” to solve the linear system in parallel. Since PETSc is under continued development, small changes in usage and calling sequences of routines will occur. PETSc is supported; see the web site http://www.mcs.anl.gov/petsc for information on contacting support. A http://www.mcs.anl.gov/petsc/publications may be found a list of publications and web sites that feature work involving PETSc. We welcome any reports of corrections for this document.« less

PETSc Users Manual Revision 3.4

DOE Office of Scientific and Technical Information (OSTI.GOV)

Balay, S.; Brown, J.; Buschelman, K.

This manual describes the use of PETSc for the numerical solution of partial differential equations and related problems on high-performance computers. The Portable, Extensible Toolkit for Scientific Computation (PETSc) is a suite of data structures and routines that provide the building blocks for the implementation of large-scale application codes on parallel (and serial) computers. PETSc uses the MPI standard for all message-passing communication. PETSc includes an expanding suite of parallel linear, nonlinear equation solvers and time integrators that may be used in application codes written in Fortran, C, C++, Python, and MATLAB (sequential). PETSc provides many of the mechanisms neededmore » within parallel application codes, such as parallel matrix and vector assembly routines. The library is organized hierarchically, enabling users to employ the level of abstraction that is most appropriate for a particular problem. By using techniques of object-oriented programming, PETSc provides enormous flexibility for users. PETSc is a sophisticated set of software tools; as such, for some users it initially has a much steeper learning curve than a simple subroutine library. In particular, for individuals without some computer science background, experience programming in C, C++ or Fortran and experience using a debugger such as gdb or dbx, it may require a significant amount of time to take full advantage of the features that enable efficient software use. However, the power of the PETSc design and the algorithms it incorporates may make the efficient implementation of many application codes simpler than “rolling them” yourself; For many tasks a package such as MATLAB is often the best tool; PETSc is not intended for the classes of problems for which effective MATLAB code can be written. PETSc also has a MATLAB interface, so portions of your code can be written in MATLAB to “try out” the PETSc solvers. The resulting code will not be scalable however because currently MATLAB is inherently not scalable; and PETSc should not be used to attempt to provide a “parallel linear solver” in an otherwise sequential code. Certainly all parts of a previously sequential code need not be parallelized but the matrix generation portion must be parallelized to expect any kind of reasonable performance. Do not expect to generate your matrix sequentially and then “use PETSc” to solve the linear system in parallel. Since PETSc is under continued development, small changes in usage and calling sequences of routines will occur. PETSc is supported; see the web site http://www.mcs.anl.gov/petsc for information on contacting support. A http://www.mcs.anl.gov/petsc/publications may be found a list of publications and web sites that feature work involving PETSc. We welcome any reports of corrections for this document.« less

PETSc Users Manual Revision 3.5

DOE Office of Scientific and Technical Information (OSTI.GOV)

Balay, S.; Abhyankar, S.; Adams, M.

This manual describes the use of PETSc for the numerical solution of partial differential equations and related problems on high-performance computers. The Portable, Extensible Toolkit for Scientific Computation (PETSc) is a suite of data structures and routines that provide the building blocks for the implementation of large-scale application codes on parallel (and serial) computers. PETSc uses the MPI standard for all message-passing communication. PETSc includes an expanding suite of parallel linear, nonlinear equation solvers and time integrators that may be used in application codes written in Fortran, C, C++, Python, and MATLAB (sequential). PETSc provides many of the mechanisms neededmore » within parallel application codes, such as parallel matrix and vector assembly routines. The library is organized hierarchically, enabling users to employ the level of abstraction that is most appropriate for a particular problem. By using techniques of object-oriented programming, PETSc provides enormous flexibility for users. PETSc is a sophisticated set of software tools; as such, for some users it initially has a much steeper learning curve than a simple subroutine library. In particular, for individuals without some computer science background, experience programming in C, C++ or Fortran and experience using a debugger such as gdb or dbx, it may require a significant amount of time to take full advantage of the features that enable efficient software use. However, the power of the PETSc design and the algorithms it incorporates may make the efficient implementation of many application codes simpler than “rolling them” yourself. ;For many tasks a package such as MATLAB is often the best tool; PETSc is not intended for the classes of problems for which effective MATLAB code can be written. PETSc also has a MATLAB interface, so portions of your code can be written in MATLAB to “try out” the PETSc solvers. The resulting code will not be scalable however because currently MATLAB is inherently not scalable; and PETSc should not be used to attempt to provide a “parallel linear solver” in an otherwise sequential code. Certainly all parts of a previously sequential code need not be parallelized but the matrix generation portion must be parallelized to expect any kind of reasonable performance. Do not expect to generate your matrix sequentially and then “use PETSc” to solve the linear system in parallel. Since PETSc is under continued development, small changes in usage and calling sequences of routines will occur. PETSc is supported; see the web site http://www.mcs.anl.gov/petsc for information on contacting support. A http://www.mcs.anl.gov/petsc/publications may be found a list of publications and web sites that feature work involving PETSc. We welcome any reports of corrections for this document.« less

Proxy-equation paradigm: A strategy for massively parallel asynchronous computations

NASA Astrophysics Data System (ADS)

Mittal, Ankita; Girimaji, Sharath

2017-09-01

Massively parallel simulations of transport equation systems call for a paradigm change in algorithm development to achieve efficient scalability. Traditional approaches require time synchronization of processing elements (PEs), which severely restricts scalability. Relaxing synchronization requirement introduces error and slows down convergence. In this paper, we propose and develop a novel "proxy equation" concept for a general transport equation that (i) tolerates asynchrony with minimal added error, (ii) preserves convergence order and thus, (iii) expected to scale efficiently on massively parallel machines. The central idea is to modify a priori the transport equation at the PE boundaries to offset asynchrony errors. Proof-of-concept computations are performed using a one-dimensional advection (convection) diffusion equation. The results demonstrate the promise and advantages of the present strategy.

Slices: A Scalable Partitioner for Finite Element Meshes

NASA Technical Reports Server (NTRS)

Ding, H. Q.; Ferraro, R. D.

1995-01-01

A parallel partitioner for partitioning unstructured finite element meshes on distributed memory architectures is developed. The element based partitioner can handle mixtures of different element types. All algorithms adopted in the partitioner are scalable, including a communication template for unpredictable incoming messages, as shown in actual timing measurements.

Performance Models for the Spike Banded Linear System Solver

DOE PAGES

Manguoglu, Murat; Saied, Faisal; Sameh, Ahmed; ...

2011-01-01

With availability of large-scale parallel platforms comprised of tens-of-thousands of processors and beyond, there is significant impetus for the development of scalable parallel sparse linear system solvers and preconditioners. An integral part of this design process is the development of performance models capable of predicting performance and providing accurate cost models for the solvers and preconditioners. There has been some work in the past on characterizing performance of the iterative solvers themselves. In this paper, we investigate the problem of characterizing performance and scalability of banded preconditioners. Recent work has demonstrated the superior convergence properties and robustness of banded preconditioners,more » compared to state-of-the-art ILU family of preconditioners as well as algebraic multigrid preconditioners. Furthermore, when used in conjunction with efficient banded solvers, banded preconditioners are capable of significantly faster time-to-solution. Our banded solver, the Truncated Spike algorithm is specifically designed for parallel performance and tolerance to deep memory hierarchies. Its regular structure is also highly amenable to accurate performance characterization. Using these characteristics, we derive the following results in this paper: (i) we develop parallel formulations of the Truncated Spike solver, (ii) we develop a highly accurate pseudo-analytical parallel performance model for our solver, (iii) we show excellent predication capabilities of our model – based on which we argue the high scalability of our solver. Our pseudo-analytical performance model is based on analytical performance characterization of each phase of our solver. These analytical models are then parameterized using actual runtime information on target platforms. An important consequence of our performance models is that they reveal underlying performance bottlenecks in both serial and parallel formulations. All of our results are validated on diverse heterogeneous multiclusters – platforms for which performance prediction is particularly challenging. Finally, we provide predict the scalability of the Spike algorithm using up to 65,536 cores with our model. In this paper we extend the results presented in the Ninth International Symposium on Parallel and Distributed Computing.« less

Using Coarrays to Parallelize Legacy Fortran Applications: Strategy and Case Study

DOE PAGES

Radhakrishnan, Hari; Rouson, Damian W. I.; Morris, Karla; ...

2015-01-01

This paper summarizes a strategy for parallelizing a legacy Fortran 77 program using the object-oriented (OO) and coarray features that entered Fortran in the 2003 and 2008 standards, respectively. OO programming (OOP) facilitates the construction of an extensible suite of model-verification and performance tests that drive the development. Coarray parallel programming facilitates a rapid evolution from a serial application to a parallel application capable of running on multicore processors and many-core accelerators in shared and distributed memory. We delineate 17 code modernization steps used to refactor and parallelize the program and study the resulting performance. Our initial studies were donemore » using the Intel Fortran compiler on a 32-core shared memory server. Scaling behavior was very poor, and profile analysis using TAU showed that the bottleneck in the performance was due to our implementation of a collective, sequential summation procedure. We were able to improve the scalability and achieve nearly linear speedup by replacing the sequential summation with a parallel, binary tree algorithm. We also tested the Cray compiler, which provides its own collective summation procedure. Intel provides no collective reductions. With Cray, the program shows linear speedup even in distributed-memory execution. We anticipate similar results with other compilers once they support the new collective procedures proposed for Fortran 2015.« less

Highly Parallel Alternating Directions Algorithm for Time Dependent Problems

NASA Astrophysics Data System (ADS)

Ganzha, M.; Georgiev, K.; Lirkov, I.; Margenov, S.; Paprzycki, M.

2011-11-01

In our work, we consider the time dependent Stokes equation on a finite time interval and on a uniform rectangular mesh, written in terms of velocity and pressure. For this problem, a parallel algorithm based on a novel direction splitting approach is developed. Here, the pressure equation is derived from a perturbed form of the continuity equation, in which the incompressibility constraint is penalized in a negative norm induced by the direction splitting. The scheme used in the algorithm is composed of two parts: (i) velocity prediction, and (ii) pressure correction. This is a Crank-Nicolson-type two-stage time integration scheme for two and three dimensional parabolic problems in which the second-order derivative, with respect to each space variable, is treated implicitly while the other variable is made explicit at each time sub-step. In order to achieve a good parallel performance the solution of the Poison problem for the pressure correction is replaced by solving a sequence of one-dimensional second order elliptic boundary value problems in each spatial direction. The parallel code is implemented using the standard MPI functions and tested on two modern parallel computer systems. The performed numerical tests demonstrate good level of parallel efficiency and scalability of the studied direction-splitting-based algorithm.

A Comparison of PETSC Library and HPF Implementations of an Archetypal PDE Computation

NASA Technical Reports Server (NTRS)

Hayder, M. Ehtesham; Keyes, David E.; Mehrotra, Piyush

1997-01-01

Two paradigms for distributed-memory parallel computation that free the application programmer from the details of message passing are compared for an archetypal structured scientific computation a nonlinear, structured-grid partial differential equation boundary value problem using the same algorithm on the same hardware. Both paradigms, parallel libraries represented by Argonne's PETSC, and parallel languages represented by the Portland Group's HPF, are found to be easy to use for this problem class, and both are reasonably effective in exploiting concurrency after a short learning curve. The level of involvement required by the application programmer under either paradigm includes specification of the data partitioning (corresponding to a geometrically simple decomposition of the domain of the PDE). Programming in SPAM style for the PETSC library requires writing the routines that discretize the PDE and its Jacobian, managing subdomain-to-processor mappings (affine global- to-local index mappings), and interfacing to library solver routines. Programming for HPF requires a complete sequential implementation of the same algorithm, introducing concurrency through subdomain blocking (an effort similar to the index mapping), and modest experimentation with rewriting loops to elucidate to the compiler the latent concurrency. Correctness and scalability are cross-validated on up to 32 nodes of an IBM SP2.

Performance prediction: A case study using a multi-ring KSR-1 machine

NASA Technical Reports Server (NTRS)

Sun, Xian-He; Zhu, Jianping

1995-01-01

While computers with tens of thousands of processors have successfully delivered high performance power for solving some of the so-called 'grand-challenge' applications, the notion of scalability is becoming an important metric in the evaluation of parallel machine architectures and algorithms. In this study, the prediction of scalability and its application are carefully investigated. A simple formula is presented to show the relation between scalability, single processor computing power, and degradation of parallelism. A case study is conducted on a multi-ring KSR1 shared virtual memory machine. Experimental and theoretical results show that the influence of topology variation of an architecture is predictable. Therefore, the performance of an algorithm on a sophisticated, heirarchical architecture can be predicted and the best algorithm-machine combination can be selected for a given application.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ghysels, Pieter; Li, Xiaoye S.; Rouet, Francois -Henry

Here, we present a sparse linear system solver that is based on a multifrontal variant of Gaussian elimination and exploits low-rank approximation of the resulting dense frontal matrices. We use hierarchically semiseparable (HSS) matrices, which have low-rank off-diagonal blocks, to approximate the frontal matrices. For HSS matrix construction, a randomized sampling algorithm is used together with interpolative decompositions. The combination of the randomized compression with a fast ULV HSS factoriz ation leads to a solver with lower computational complexity than the standard multifrontal method for many applications, resulting in speedups up to 7 fold for problems in our test suite.more » The implementation targets many-core systems by using task parallelism with dynamic runtime scheduling. Numerical experiments show performance improvements over state-of-the-art sparse direct solvers. The implementation achieves high performance and good scalability on a range of modern shared memory parallel systems, including the Intel Xeon Phi (MIC). The code is part of a software package called STRUMPACK - STRUctured Matrices PACKage, which also has a distributed memory component for dense rank-structured matrices.« less

MaMiCo: Software design for parallel molecular-continuum flow simulations

NASA Astrophysics Data System (ADS)

Neumann, Philipp; Flohr, Hanno; Arora, Rahul; Jarmatz, Piet; Tchipev, Nikola; Bungartz, Hans-Joachim

2016-03-01

The macro-micro-coupling tool (MaMiCo) was developed to ease the development of and modularize molecular-continuum simulations, retaining sequential and parallel performance. We demonstrate the functionality and performance of MaMiCo by coupling the spatially adaptive Lattice Boltzmann framework waLBerla with four molecular dynamics (MD) codes: the light-weight Lennard-Jones-based implementation SimpleMD, the node-level optimized software ls1 mardyn, and the community codes ESPResSo and LAMMPS. We detail interface implementations to connect each solver with MaMiCo. The coupling for each waLBerla-MD setup is validated in three-dimensional channel flow simulations which are solved by means of a state-based coupling method. We provide sequential and strong scaling measurements for the four molecular-continuum simulations. The overhead of MaMiCo is found to come at 10%-20% of the total (MD) runtime. The measurements further show that scalability of the hybrid simulations is reached on up to 500 Intel SandyBridge, and more than 1000 AMD Bulldozer compute cores.

JuxtaView - A tool for interactive visualization of large imagery on scalable tiled displays

USGS Publications Warehouse

Krishnaprasad, N.K.; Vishwanath, V.; Venkataraman, S.; Rao, A.G.; Renambot, L.; Leigh, J.; Johnson, A.E.; Davis, B.

2004-01-01

JuxtaView is a cluster-based application for viewing ultra-high-resolution images on scalable tiled displays. We present in JuxtaView, a new parallel computing and distributed memory approach for out-of-core montage visualization, using LambdaRAM, a software-based network-level cache system. The ultimate goal of JuxtaView is to enable a user to interactively roam through potentially terabytes of distributed, spatially referenced image data such as those from electron microscopes, satellites and aerial photographs. In working towards this goal, we describe our first prototype implemented over a local area network, where the image is distributed using LambdaRAM, on the memory of all nodes of a PC cluster driving a tiled display wall. Aggressive pre-fetching schemes employed by LambdaRAM help to reduce latency involved in remote memory access. We compare LambdaRAM with a more traditional memory-mapped file approach for out-of-core visualization. ?? 2004 IEEE.

A High-Speed Design of Montgomery Multiplier

NASA Astrophysics Data System (ADS)

Fan, Yibo; Ikenaga, Takeshi; Goto, Satoshi

With the increase of key length used in public cryptographic algorithms such as RSA and ECC, the speed of Montgomery multiplication becomes a bottleneck. This paper proposes a high speed design of Montgomery multiplier. Firstly, a modified scalable high-radix Montgomery algorithm is proposed to reduce critical path. Secondly, a high-radix clock-saving dataflow is proposed to support high-radix operation and one clock cycle delay in dataflow. Finally, a hardware-reused architecture is proposed to reduce the hardware cost and a parallel radix-16 design of data path is proposed to accelerate the speed. By using HHNEC 0.25μm standard cell library, the implementation results show that the total cost of Montgomery multiplier is 130 KGates, the clock frequency is 180MHz and the throughput of 1024-bit RSA encryption is 352kbps. This design is suitable to be used in high speed RSA or ECC encryption/decryption. As a scalable design, it supports any key-length encryption/decryption up to the size of on-chip memory.

Multiresolution molecular mechanics: Implementation and efficiency

NASA Astrophysics Data System (ADS)

Biyikli, Emre; To, Albert C.

2017-01-01

Atomistic/continuum coupling methods combine accurate atomistic methods and efficient continuum methods to simulate the behavior of highly ordered crystalline systems. Coupled methods utilize the advantages of both approaches to simulate systems at a lower computational cost, while retaining the accuracy associated with atomistic methods. Many concurrent atomistic/continuum coupling methods have been proposed in the past; however, their true computational efficiency has not been demonstrated. The present work presents an efficient implementation of a concurrent coupling method called the Multiresolution Molecular Mechanics (MMM) for serial, parallel, and adaptive analysis. First, we present the features of the software implemented along with the associated technologies. The scalability of the software implementation is demonstrated, and the competing effects of multiscale modeling and parallelization are discussed. Then, the algorithms contributing to the efficiency of the software are presented. These include algorithms for eliminating latent ghost atoms from calculations and measurement-based dynamic balancing of parallel workload. The efficiency improvements made by these algorithms are demonstrated by benchmark tests. The efficiency of the software is found to be on par with LAMMPS, a state-of-the-art Molecular Dynamics (MD) simulation code, when performing full atomistic simulations. Speed-up of the MMM method is shown to be directly proportional to the reduction of the number of the atoms visited in force computation. Finally, an adaptive MMM analysis on a nanoindentation problem, containing over a million atoms, is performed, yielding an improvement of 6.3-8.5 times in efficiency, over the full atomistic MD method. For the first time, the efficiency of a concurrent atomistic/continuum coupling method is comprehensively investigated and demonstrated.

A FPGA Implementation of the CAR-FAC Cochlear Model.

PubMed

Xu, Ying; Thakur, Chetan S; Singh, Ram K; Hamilton, Tara Julia; Wang, Runchun M; van Schaik, André

2018-01-01

This paper presents a digital implementation of the Cascade of Asymmetric Resonators with Fast-Acting Compression (CAR-FAC) cochlear model. The CAR part simulates the basilar membrane's (BM) response to sound. The FAC part models the outer hair cell (OHC), the inner hair cell (IHC), and the medial olivocochlear efferent system functions. The FAC feeds back to the CAR by moving the poles and zeros of the CAR resonators automatically. We have implemented a 70-section, 44.1 kHz sampling rate CAR-FAC system on an Altera Cyclone V Field Programmable Gate Array (FPGA) with 18% ALM utilization by using time-multiplexing and pipeline parallelizing techniques and present measurement results here. The fully digital reconfigurable CAR-FAC system is stable, scalable, easy to use, and provides an excellent input stage to more complex machine hearing tasks such as sound localization, sound segregation, speech recognition, and so on.

A FPGA Implementation of the CAR-FAC Cochlear Model

PubMed Central

Xu, Ying; Thakur, Chetan S.; Singh, Ram K.; Hamilton, Tara Julia; Wang, Runchun M.; van Schaik, André

2018-01-01

This paper presents a digital implementation of the Cascade of Asymmetric Resonators with Fast-Acting Compression (CAR-FAC) cochlear model. The CAR part simulates the basilar membrane's (BM) response to sound. The FAC part models the outer hair cell (OHC), the inner hair cell (IHC), and the medial olivocochlear efferent system functions. The FAC feeds back to the CAR by moving the poles and zeros of the CAR resonators automatically. We have implemented a 70-section, 44.1 kHz sampling rate CAR-FAC system on an Altera Cyclone V Field Programmable Gate Array (FPGA) with 18% ALM utilization by using time-multiplexing and pipeline parallelizing techniques and present measurement results here. The fully digital reconfigurable CAR-FAC system is stable, scalable, easy to use, and provides an excellent input stage to more complex machine hearing tasks such as sound localization, sound segregation, speech recognition, and so on. PMID:29692700

FPGA-based coprocessor for matrix algorithms implementation

NASA Astrophysics Data System (ADS)

Amira, Abbes; Bensaali, Faycal

2003-03-01

Matrix algorithms are important in many types of applications including image and signal processing. These areas require enormous computing power. A close examination of the algorithms used in these, and related, applications reveals that many of the fundamental actions involve matrix operations such as matrix multiplication which is of O (N3) on a sequential computer and O (N3/p) on a parallel system with p processors complexity. This paper presents an investigation into the design and implementation of different matrix algorithms such as matrix operations, matrix transforms and matrix decompositions using an FPGA based environment. Solutions for the problem of processing large matrices have been proposed. The proposed system architectures are scalable, modular and require less area and time complexity with reduced latency when compared with existing structures.

Matrix multiplication on the Intel Touchstone Delta

DOE Office of Scientific and Technical Information (OSTI.GOV)

Huss-Lederman, S.; Jacobson, E.M.; Tsao, A.

1993-12-31

Matrix multiplication is a key primitive in block matrix algorithms such as those found in LAPACK. We present results from our study of matrix multiplication algorithms on the Intel Touchstone Delta, a distributed memory message-passing architecture with a two-dimensional mesh topology. We obtain an implementation that uses communication primitives highly suited to the Delta and exploits the single node assembly-coded matrix multiplication. Our algorithm is completely general, able to deal with arbitrary mesh aspect ratios and matrix dimensions, and has achieved parallel efficiency of 86% with overall peak performance in excess of 8 Gflops on 256 nodes for an 8800more » {times} 8800 matrix. We describe our algorithm design and implementation, and present performance results that demonstrate scalability and robust behavior over varying mesh topologies.« less

Mapping of H.264 decoding on a multiprocessor architecture

NASA Astrophysics Data System (ADS)

van der Tol, Erik B.; Jaspers, Egbert G.; Gelderblom, Rob H.

2003-05-01

Due to the increasing significance of development costs in the competitive domain of high-volume consumer electronics, generic solutions are required to enable reuse of the design effort and to increase the potential market volume. As a result from this, Systems-on-Chip (SoCs) contain a growing amount of fully programmable media processing devices as opposed to application-specific systems, which offered the most attractive solutions due to a high performance density. The following motivates this trend. First, SoCs are increasingly dominated by their communication infrastructure and embedded memory, thereby making the cost of the functional units less significant. Moreover, the continuously growing design costs require generic solutions that can be applied over a broad product range. Hence, powerful programmable SoCs are becoming increasingly attractive. However, to enable power-efficient designs, that are also scalable over the advancing VLSI technology, parallelism should be fully exploited. Both task-level and instruction-level parallelism can be provided by means of e.g. a VLIW multiprocessor architecture. To provide the above-mentioned scalability, we propose to partition the data over the processors, instead of traditional functional partitioning. An advantage of this approach is the inherent locality of data, which is extremely important for communication-efficient software implementations. Consequently, a software implementation is discussed, enabling e.g. SD resolution H.264 decoding with a two-processor architecture, whereas High-Definition (HD) decoding can be achieved with an eight-processor system, executing the same software. Experimental results show that the data communication considerably reduces up to 65% directly improving the overall performance. Apart from considerable improvement in memory bandwidth, this novel concept of partitioning offers a natural approach for optimally balancing the load of all processors, thereby further improving the overall speedup.

GASPRNG: GPU accelerated scalable parallel random number generator library

NASA Astrophysics Data System (ADS)

Gao, Shuang; Peterson, Gregory D.

2013-04-01

Graphics processors represent a promising technology for accelerating computational science applications. Many computational science applications require fast and scalable random number generation with good statistical properties, so they use the Scalable Parallel Random Number Generators library (SPRNG). We present the GPU Accelerated SPRNG library (GASPRNG) to accelerate SPRNG in GPU-based high performance computing systems. GASPRNG includes code for a host CPU and CUDA code for execution on NVIDIA graphics processing units (GPUs) along with a programming interface to support various usage models for pseudorandom numbers and computational science applications executing on the CPU, GPU, or both. This paper describes the implementation approach used to produce high performance and also describes how to use the programming interface. The programming interface allows a user to be able to use GASPRNG the same way as SPRNG on traditional serial or parallel computers as well as to develop tightly coupled programs executing primarily on the GPU. We also describe how to install GASPRNG and use it. To help illustrate linking with GASPRNG, various demonstration codes are included for the different usage models. GASPRNG on a single GPU shows up to 280x speedup over SPRNG on a single CPU core and is able to scale for larger systems in the same manner as SPRNG. Because GASPRNG generates identical streams of pseudorandom numbers as SPRNG, users can be confident about the quality of GASPRNG for scalable computational science applications. Catalogue identifier: AEOI_v1_0 Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AEOI_v1_0.html Program obtainable from: CPC Program Library, Queen’s University, Belfast, N. Ireland Licensing provisions: UTK license. No. of lines in distributed program, including test data, etc.: 167900 No. of bytes in distributed program, including test data, etc.: 1422058 Distribution format: tar.gz Programming language: C and CUDA. Computer: Any PC or workstation with NVIDIA GPU (Tested on Fermi GTX480, Tesla C1060, Tesla M2070). Operating system: Linux with CUDA version 4.0 or later. Should also run on MacOS, Windows, or UNIX. Has the code been vectorized or parallelized?: Yes. Parallelized using MPI directives. RAM: 512 MB˜ 732 MB (main memory on host CPU, depending on the data type of random numbers.) / 512 MB (GPU global memory) Classification: 4.13, 6.5. Nature of problem: Many computational science applications are able to consume large numbers of random numbers. For example, Monte Carlo simulations are able to consume limitless random numbers for the computation as long as resources for the computing are supported. Moreover, parallel computational science applications require independent streams of random numbers to attain statistically significant results. The SPRNG library provides this capability, but at a significant computational cost. The GASPRNG library presented here accelerates the generators of independent streams of random numbers using graphical processing units (GPUs). Solution method: Multiple copies of random number generators in GPUs allow a computational science application to consume large numbers of random numbers from independent, parallel streams. GASPRNG is a random number generators library to allow a computational science application to employ multiple copies of random number generators to boost performance. Users can interface GASPRNG with software code executing on microprocessors and/or GPUs. Running time: The tests provided take a few minutes to run.

Parallel implementation of the particle simulation method with dynamic load balancing: Toward realistic geodynamical simulation

NASA Astrophysics Data System (ADS)

Furuichi, M.; Nishiura, D.

2015-12-01

Fully Lagrangian methods such as Smoothed Particle Hydrodynamics (SPH) and Discrete Element Method (DEM) have been widely used to solve the continuum and particles motions in the computational geodynamics field. These mesh-free methods are suitable for the problems with the complex geometry and boundary. In addition, their Lagrangian nature allows non-diffusive advection useful for tracking history dependent properties (e.g. rheology) of the material. These potential advantages over the mesh-based methods offer effective numerical applications to the geophysical flow and tectonic processes, which are for example, tsunami with free surface and floating body, magma intrusion with fracture of rock, and shear zone pattern generation of granular deformation. In order to investigate such geodynamical problems with the particle based methods, over millions to billion particles are required for the realistic simulation. Parallel computing is therefore important for handling such huge computational cost. An efficient parallel implementation of SPH and DEM methods is however known to be difficult especially for the distributed-memory architecture. Lagrangian methods inherently show workload imbalance problem for parallelization with the fixed domain in space, because particles move around and workloads change during the simulation. Therefore dynamic load balance is key technique to perform the large scale SPH and DEM simulation. In this work, we present the parallel implementation technique of SPH and DEM method utilizing dynamic load balancing algorithms toward the high resolution simulation over large domain using the massively parallel super computer system. Our method utilizes the imbalances of the executed time of each MPI process as the nonlinear term of parallel domain decomposition and minimizes them with the Newton like iteration method. In order to perform flexible domain decomposition in space, the slice-grid algorithm is used. Numerical tests show that our approach is suitable for solving the particles with different calculation costs (e.g. boundary particles) as well as the heterogeneous computer architecture. We analyze the parallel efficiency and scalability on the super computer systems (K-computer, Earth simulator 3, etc.).

A scalable neuroinformatics data flow for electrophysiological signals using MapReduce.

PubMed

Jayapandian, Catherine; Wei, Annan; Ramesh, Priya; Zonjy, Bilal; Lhatoo, Samden D; Loparo, Kenneth; Zhang, Guo-Qiang; Sahoo, Satya S

2015-01-01

Data-driven neuroscience research is providing new insights in progression of neurological disorders and supporting the development of improved treatment approaches. However, the volume, velocity, and variety of neuroscience data generated from sophisticated recording instruments and acquisition methods have exacerbated the limited scalability of existing neuroinformatics tools. This makes it difficult for neuroscience researchers to effectively leverage the growing multi-modal neuroscience data to advance research in serious neurological disorders, such as epilepsy. We describe the development of the Cloudwave data flow that uses new data partitioning techniques to store and analyze electrophysiological signal in distributed computing infrastructure. The Cloudwave data flow uses MapReduce parallel programming algorithm to implement an integrated signal data processing pipeline that scales with large volume of data generated at high velocity. Using an epilepsy domain ontology together with an epilepsy focused extensible data representation format called Cloudwave Signal Format (CSF), the data flow addresses the challenge of data heterogeneity and is interoperable with existing neuroinformatics data representation formats, such as HDF5. The scalability of the Cloudwave data flow is evaluated using a 30-node cluster installed with the open source Hadoop software stack. The results demonstrate that the Cloudwave data flow can process increasing volume of signal data by leveraging Hadoop Data Nodes to reduce the total data processing time. The Cloudwave data flow is a template for developing highly scalable neuroscience data processing pipelines using MapReduce algorithms to support a variety of user applications.

A scalable neuroinformatics data flow for electrophysiological signals using MapReduce

PubMed Central

Jayapandian, Catherine; Wei, Annan; Ramesh, Priya; Zonjy, Bilal; Lhatoo, Samden D.; Loparo, Kenneth; Zhang, Guo-Qiang; Sahoo, Satya S.

2015-01-01

Data-driven neuroscience research is providing new insights in progression of neurological disorders and supporting the development of improved treatment approaches. However, the volume, velocity, and variety of neuroscience data generated from sophisticated recording instruments and acquisition methods have exacerbated the limited scalability of existing neuroinformatics tools. This makes it difficult for neuroscience researchers to effectively leverage the growing multi-modal neuroscience data to advance research in serious neurological disorders, such as epilepsy. We describe the development of the Cloudwave data flow that uses new data partitioning techniques to store and analyze electrophysiological signal in distributed computing infrastructure. The Cloudwave data flow uses MapReduce parallel programming algorithm to implement an integrated signal data processing pipeline that scales with large volume of data generated at high velocity. Using an epilepsy domain ontology together with an epilepsy focused extensible data representation format called Cloudwave Signal Format (CSF), the data flow addresses the challenge of data heterogeneity and is interoperable with existing neuroinformatics data representation formats, such as HDF5. The scalability of the Cloudwave data flow is evaluated using a 30-node cluster installed with the open source Hadoop software stack. The results demonstrate that the Cloudwave data flow can process increasing volume of signal data by leveraging Hadoop Data Nodes to reduce the total data processing time. The Cloudwave data flow is a template for developing highly scalable neuroscience data processing pipelines using MapReduce algorithms to support a variety of user applications. PMID:25852536

Implementation of the DPM Monte Carlo code on a parallel architecture for treatment planning applications.

PubMed

Tyagi, Neelam; Bose, Abhijit; Chetty, Indrin J

2004-09-01

We have parallelized the Dose Planning Method (DPM), a Monte Carlo code optimized for radiotherapy class problems, on distributed-memory processor architectures using the Message Passing Interface (MPI). Parallelization has been investigated on a variety of parallel computing architectures at the University of Michigan-Center for Advanced Computing, with respect to efficiency and speedup as a function of the number of processors. We have integrated the parallel pseudo random number generator from the Scalable Parallel Pseudo-Random Number Generator (SPRNG) library to run with the parallel DPM. The Intel cluster consisting of 800 MHz Intel Pentium III processor shows an almost linear speedup up to 32 processors for simulating 1 x 10(8) or more particles. The speedup results are nearly linear on an Athlon cluster (up to 24 processors based on availability) which consists of 1.8 GHz+ Advanced Micro Devices (AMD) Athlon processors on increasing the problem size up to 8 x 10(8) histories. For a smaller number of histories (1 x 10(8)) the reduction of efficiency with the Athlon cluster (down to 83.9% with 24 processors) occurs because the processing time required to simulate 1 x 10(8) histories is less than the time associated with interprocessor communication. A similar trend was seen with the Opteron Cluster (consisting of 1400 MHz, 64-bit AMD Opteron processors) on increasing the problem size. Because of the 64-bit architecture Opteron processors are capable of storing and processing instructions at a faster rate and hence are faster as compared to the 32-bit Athlon processors. We have validated our implementation with an in-phantom dose calculation study using a parallel pencil monoenergetic electron beam of 20 MeV energy. The phantom consists of layers of water, lung, bone, aluminum, and titanium. The agreement in the central axis depth dose curves and profiles at different depths shows that the serial and parallel codes are equivalent in accuracy.

ConnectX2 In niBand Management Queues: New support for Network Of oaded

DOE Office of Scientific and Technical Information (OSTI.GOV)

Graham, Richard L; Poole, Stephen W; Shamis, Pavel

2010-01-01

This paper introduces the newly developed InfiniBand (IB) Management Queue capability, used by the Host Channel Adapter (HCA) to manage network task data flow dependancies, and progress the communications associated with such flows. These tasks include sends, receives, and the newly supported wait task, and are scheduled by the HCA based on a data dependency description provided by the user. This functionality is supported by the ConnectX-2 HCA, and provides the means for delegating collective communication management and progress to the HCA, also known as collective communication offload. This provides a means for overlapping collective communications managed by the HCAmore » and computation on the Central Processing Unit (CPU), thus making it possible to reduce the impact of system noise on parallel applications using collective operations. This paper further describes how this new capability can be used to implement scalable Message Passing Interface (MPI) collective operations, describing the high level details of how this new capability is used to implement the MPI Barrier collective operation, focusing on the latency sensitive performance aspects of this new capability. This paper concludes with small scale benchmark experiments comparing implementations of the barrier collective operation, using the new network offload capabilities, with established point-to-point based implementations of these same algorithms, which manage the data flow using the central processing unit. These early results demonstrate the promise this new capability provides to improve the scalability of high-performance applications using collective communications. The latency of the HCA based implementation of the barrier is similar to that of the best performing point-to-point based implementation managed by the central processing unit, starting to outperform these as the number of processes involved in the collective operation increases.« less

ConnectX-2 InfiniBand Management Queues: First Investigation of the New Support for Network Offloaded Collective Operations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Graham, Richard L; Poole, Stephen W; Shamis, Pavel

2010-01-01

This paper introduces the newly developed Infini-Band (IB) Management Queue capability, used by the Host Channel Adapter (HCA) to manage network task data flow dependancies, and progress the communications associated with such flows. These tasks include sends, receives, and the newly supported wait task, and are scheduled by the HCA based on a data dependency description provided by the user. This functionality is supported by the ConnectX-2 HCA, and provides the means for delegating collective communication management and progress to the HCA, also known as collective communication offload. This provides a means for overlapping collective communications managed by the HCAmore » and computation on the Central Processing Unit (CPU), thus making it possible to reduce the impact of system noise on parallel applications using collective operations. This paper further describes how this new capability can be used to implement scalable Message Passing Interface (MPI) collective operations, describing the high level details of how this new capability is used to implement the MPI Barrier collective operation, focusing on the latency sensitive performance aspects of this new capability. This paper concludes with small scale benchmark experiments comparing implementations of the barrier collective operation, using the new network offload capabilities, with established point-to-point based implementations of these same algorithms, which manage the data flow using the central processing unit. These early results demonstrate the promise this new capability provides to improve the scalability of high performance applications using collective communications. The latency of the HCA based implementation of the barrier is similar to that of the best performing point-to-point based implementation managed by the central processing unit, starting to outperform these as the number of processes involved in the collective operation increases.« less

A Metascalable Computing Framework for Large Spatiotemporal-Scale Atomistic Simulations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Nomura, K; Seymour, R; Wang, W

2009-02-17

A metascalable (or 'design once, scale on new architectures') parallel computing framework has been developed for large spatiotemporal-scale atomistic simulations of materials based on spatiotemporal data locality principles, which is expected to scale on emerging multipetaflops architectures. The framework consists of: (1) an embedded divide-and-conquer (EDC) algorithmic framework based on spatial locality to design linear-scaling algorithms for high complexity problems; (2) a space-time-ensemble parallel (STEP) approach based on temporal locality to predict long-time dynamics, while introducing multiple parallelization axes; and (3) a tunable hierarchical cellular decomposition (HCD) parallelization framework to map these O(N) algorithms onto a multicore cluster based onmore » hybrid implementation combining message passing and critical section-free multithreading. The EDC-STEP-HCD framework exposes maximal concurrency and data locality, thereby achieving: (1) inter-node parallel efficiency well over 0.95 for 218 billion-atom molecular-dynamics and 1.68 trillion electronic-degrees-of-freedom quantum-mechanical simulations on 212,992 IBM BlueGene/L processors (superscalability); (2) high intra-node, multithreading parallel efficiency (nanoscalability); and (3) nearly perfect time/ensemble parallel efficiency (eon-scalability). The spatiotemporal scale covered by MD simulation on a sustained petaflops computer per day (i.e. petaflops {center_dot} day of computing) is estimated as NT = 2.14 (e.g. N = 2.14 million atoms for T = 1 microseconds).« less

An efficient parallel algorithm: Poststack and prestack Kirchhoff 3D depth migration using flexi-depth iterations

NASA Astrophysics Data System (ADS)

Rastogi, Richa; Srivastava, Abhishek; Khonde, Kiran; Sirasala, Kirannmayi M.; Londhe, Ashutosh; Chavhan, Hitesh

2015-07-01

This paper presents an efficient parallel 3D Kirchhoff depth migration algorithm suitable for current class of multicore architecture. The fundamental Kirchhoff depth migration algorithm exhibits inherent parallelism however, when it comes to 3D data migration, as the data size increases the resource requirement of the algorithm also increases. This challenges its practical implementation even on current generation high performance computing systems. Therefore a smart parallelization approach is essential to handle 3D data for migration. The most compute intensive part of Kirchhoff depth migration algorithm is the calculation of traveltime tables due to its resource requirements such as memory/storage and I/O. In the current research work, we target this area and develop a competent parallel algorithm for post and prestack 3D Kirchhoff depth migration, using hybrid MPI+OpenMP programming techniques. We introduce a concept of flexi-depth iterations while depth migrating data in parallel imaging space, using optimized traveltime table computations. This concept provides flexibility to the algorithm by migrating data in a number of depth iterations, which depends upon the available node memory and the size of data to be migrated during runtime. Furthermore, it minimizes the requirements of storage, I/O and inter-node communication, thus making it advantageous over the conventional parallelization approaches. The developed parallel algorithm is demonstrated and analysed on Yuva II, a PARAM series of supercomputers. Optimization, performance and scalability experiment results along with the migration outcome show the effectiveness of the parallel algorithm.

Scalable problems and memory bounded speedup

NASA Technical Reports Server (NTRS)

Sun, Xian-He; Ni, Lionel M.

1992-01-01

In this paper three models of parallel speedup are studied. They are fixed-size speedup, fixed-time speedup and memory-bounded speedup. The latter two consider the relationship between speedup and problem scalability. Two sets of speedup formulations are derived for these three models. One set considers uneven workload allocation and communication overhead and gives more accurate estimation. Another set considers a simplified case and provides a clear picture on the impact of the sequential portion of an application on the possible performance gain from parallel processing. The simplified fixed-size speedup is Amdahl's law. The simplified fixed-time speedup is Gustafson's scaled speedup. The simplified memory-bounded speedup contains both Amdahl's law and Gustafson's scaled speedup as special cases. This study leads to a better understanding of parallel processing.

Parallel scalability of Hartree-Fock calculations

NASA Astrophysics Data System (ADS)

Chow, Edmond; Liu, Xing; Smelyanskiy, Mikhail; Hammond, Jeff R.

2015-03-01

Quantum chemistry is increasingly performed using large cluster computers consisting of multiple interconnected nodes. For a fixed molecular problem, the efficiency of a calculation usually decreases as more nodes are used, due to the cost of communication between the nodes. This paper empirically investigates the parallel scalability of Hartree-Fock calculations. The construction of the Fock matrix and the density matrix calculation are analyzed separately. For the former, we use a parallelization of Fock matrix construction based on a static partitioning of work followed by a work stealing phase. For the latter, we use density matrix purification from the linear scaling methods literature, but without using sparsity. When using large numbers of nodes for moderately sized problems, density matrix computations are network-bandwidth bound, making purification methods potentially faster than eigendecomposition methods.

Scalable Parallel Density-based Clustering and Applications

NASA Astrophysics Data System (ADS)

Patwary, Mostofa Ali

2014-04-01

Recently, density-based clustering algorithms (DBSCAN and OPTICS) have gotten significant attention of the scientific community due to their unique capability of discovering arbitrary shaped clusters and eliminating noise data. These algorithms have several applications, which require high performance computing, including finding halos and subhalos (clusters) from massive cosmology data in astrophysics, analyzing satellite images, X-ray crystallography, and anomaly detection. However, parallelization of these algorithms are extremely challenging as they exhibit inherent sequential data access order, unbalanced workload resulting in low parallel efficiency. To break the data access sequentiality and to achieve high parallelism, we develop new parallel algorithms, both for DBSCAN and OPTICS, designed using graph algorithmic techniques. For example, our parallel DBSCAN algorithm exploits the similarities between DBSCAN and computing connected components. Using datasets containing up to a billion floating point numbers, we show that our parallel density-based clustering algorithms significantly outperform the existing algorithms, achieving speedups up to 27.5 on 40 cores on shared memory architecture and speedups up to 5,765 using 8,192 cores on distributed memory architecture. In our experiments, we found that while achieving the scalability, our algorithms produce clustering results with comparable quality to the classical algorithms.

Cross-scale efficient tensor contractions for coupled cluster computations through multiple programming model backends

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ibrahim, Khaled Z.; Epifanovsky, Evgeny; Williams, Samuel

Coupled-cluster methods provide highly accurate models of molecular structure through explicit numerical calculation of tensors representing the correlation between electrons. These calculations are dominated by a sequence of tensor contractions, motivating the development of numerical libraries for such operations. While based on matrix–matrix multiplication, these libraries are specialized to exploit symmetries in the molecular structure and in electronic interactions, and thus reduce the size of the tensor representation and the complexity of contractions. The resulting algorithms are irregular and their parallelization has been previously achieved via the use of dynamic scheduling or specialized data decompositions. We introduce our efforts tomore » extend the Libtensor framework to work in the distributed memory environment in a scalable and energy-efficient manner. We achieve up to 240× speedup compared with the optimized shared memory implementation of Libtensor. We attain scalability to hundreds of thousands of compute cores on three distributed-memory architectures (Cray XC30 and XC40, and IBM Blue Gene/Q), and on a heterogeneous GPU-CPU system (Cray XK7). As the bottlenecks shift from being compute-bound DGEMM's to communication-bound collectives as the size of the molecular system scales, we adopt two radically different parallelization approaches for handling load-imbalance, tasking and bulk synchronous models. Nevertheless, we preserve a unified interface to both programming models to maintain the productivity of computational quantum chemists.« less

Cross-scale efficient tensor contractions for coupled cluster computations through multiple programming model backends

DOE PAGES

Ibrahim, Khaled Z.; Epifanovsky, Evgeny; Williams, Samuel; ...

2017-03-08

Coupled-cluster methods provide highly accurate models of molecular structure through explicit numerical calculation of tensors representing the correlation between electrons. These calculations are dominated by a sequence of tensor contractions, motivating the development of numerical libraries for such operations. While based on matrix–matrix multiplication, these libraries are specialized to exploit symmetries in the molecular structure and in electronic interactions, and thus reduce the size of the tensor representation and the complexity of contractions. The resulting algorithms are irregular and their parallelization has been previously achieved via the use of dynamic scheduling or specialized data decompositions. We introduce our efforts tomore » extend the Libtensor framework to work in the distributed memory environment in a scalable and energy-efficient manner. We achieve up to 240× speedup compared with the optimized shared memory implementation of Libtensor. We attain scalability to hundreds of thousands of compute cores on three distributed-memory architectures (Cray XC30 and XC40, and IBM Blue Gene/Q), and on a heterogeneous GPU-CPU system (Cray XK7). As the bottlenecks shift from being compute-bound DGEMM's to communication-bound collectives as the size of the molecular system scales, we adopt two radically different parallelization approaches for handling load-imbalance, tasking and bulk synchronous models. Nevertheless, we preserve a unified interface to both programming models to maintain the productivity of computational quantum chemists.« less

Network selection, Information filtering and Scalable computation

NASA Astrophysics Data System (ADS)

Ye, Changqing

This dissertation explores two application scenarios of sparsity pursuit method on large scale data sets. The first scenario is classification and regression in analyzing high dimensional structured data, where predictors corresponds to nodes of a given directed graph. This arises in, for instance, identification of disease genes for the Parkinson's diseases from a network of candidate genes. In such a situation, directed graph describes dependencies among the genes, where direction of edges represent certain causal effects. Key to high-dimensional structured classification and regression is how to utilize dependencies among predictors as specified by directions of the graph. In this dissertation, we develop a novel method that fully takes into account such dependencies formulated through certain nonlinear constraints. We apply the proposed method to two applications, feature selection in large margin binary classification and in linear regression. We implement the proposed method through difference convex programming for the cost function and constraints. Finally, theoretical and numerical analyses suggest that the proposed method achieves the desired objectives. An application to disease gene identification is presented. The second application scenario is personalized information filtering which extracts the information specifically relevant to a user, predicting his/her preference over a large number of items, based on the opinions of users who think alike or its content. This problem is cast into the framework of regression and classification, where we introduce novel partial latent models to integrate additional user-specific and content-specific predictors, for higher predictive accuracy. In particular, we factorize a user-over-item preference matrix into a product of two matrices, each representing a user's preference and an item preference by users. Then we propose a likelihood method to seek a sparsest latent factorization, from a class of over-complete factorizations, possibly with a high percentage of missing values. This promotes additional sparsity beyond rank reduction. Computationally, we design methods based on a ``decomposition and combination'' strategy, to break large-scale optimization into many small subproblems to solve in a recursive and parallel manner. On this basis, we implement the proposed methods through multi-platform shared-memory parallel programming, and through Mahout, a library for scalable machine learning and data mining, for mapReduce computation. For example, our methods are scalable to a dataset consisting of three billions of observations on a single machine with sufficient memory, having good timings. Both theoretical and numerical investigations show that the proposed methods exhibit significant improvement in accuracy over state-of-the-art scalable methods.

: A Scalable and Transparent System for Simulating MPI Programs

DOE Office of Scientific and Technical Information (OSTI.GOV)

Perumalla, Kalyan S

2010-01-01

is a scalable, transparent system for experimenting with the execution of parallel programs on simulated computing platforms. The level of simulated detail can be varied for application behavior as well as for machine characteristics. Unique features of are repeatability of execution, scalability to millions of simulated (virtual) MPI ranks, scalability to hundreds of thousands of host (real) MPI ranks, portability of the system to a variety of host supercomputing platforms, and the ability to experiment with scientific applications whose source-code is available. The set of source-code interfaces supported by is being expanded to support a wider set of applications, andmore » MPI-based scientific computing benchmarks are being ported. In proof-of-concept experiments, has been successfully exercised to spawn and sustain very large-scale executions of an MPI test program given in source code form. Low slowdowns are observed, due to its use of purely discrete event style of execution, and due to the scalability and efficiency of the underlying parallel discrete event simulation engine, sik. In the largest runs, has been executed on up to 216,000 cores of a Cray XT5 supercomputer, successfully simulating over 27 million virtual MPI ranks, each virtual rank containing its own thread context, and all ranks fully synchronized by virtual time.« less

Programming with BIG data in R: Scaling analytics from one to thousands of nodes

DOE Office of Scientific and Technical Information (OSTI.GOV)

Schmidt, Drew; Chen, Wei -Chen; Matheson, Michael A.

Here, we present a tutorial overview showing how one can achieve scalable performance with R. We do so by utilizing several package extensions, including those from the pbdR project. These packages consist of high performance, high-level interfaces to and extensions of MPI, PBLAS, ScaLAPACK, I/O libraries, profiling libraries, and more. While these libraries shine brightest on large distributed platforms, they also work rather well on small clusters and often, surprisingly, even on a laptop with only two cores. Our tutorial begins with recommendations on how to get more performance out of your R code before considering parallel implementations. Because Rmore » is a high-level language, a function can have a deep hierarchy of operations. For big data, this can easily lead to inefficiency. Profiling is an important tool to understand the performance of an R code for both serial and parallel improvements.« less

DOE Office of Scientific and Technical Information (OSTI.GOV)

Luszczek, Piotr R; Tomov, Stanimire Z; Dongarra, Jack J

We present an efficient and scalable programming model for the development of linear algebra in heterogeneous multi-coprocessor environments. The model incorporates some of the current best design and implementation practices for the heterogeneous acceleration of dense linear algebra (DLA). Examples are given as the basis for solving linear systems' algorithms - the LU, QR, and Cholesky factorizations. To generate the extreme level of parallelism needed for the efficient use of coprocessors, algorithms of interest are redesigned and then split into well-chosen computational tasks. The tasks execution is scheduled over the computational components of a hybrid system of multi-core CPUs andmore » coprocessors using a light-weight runtime system. The use of lightweight runtime systems keeps scheduling overhead low, while enabling the expression of parallelism through otherwise sequential code. This simplifies the development efforts and allows the exploration of the unique strengths of the various hardware components.« less

Programming with BIG data in R: Scaling analytics from one to thousands of nodes

DOE PAGES

Schmidt, Drew; Chen, Wei -Chen; Matheson, Michael A.; ...

2016-11-09

Here, we present a tutorial overview showing how one can achieve scalable performance with R. We do so by utilizing several package extensions, including those from the pbdR project. These packages consist of high performance, high-level interfaces to and extensions of MPI, PBLAS, ScaLAPACK, I/O libraries, profiling libraries, and more. While these libraries shine brightest on large distributed platforms, they also work rather well on small clusters and often, surprisingly, even on a laptop with only two cores. Our tutorial begins with recommendations on how to get more performance out of your R code before considering parallel implementations. Because Rmore » is a high-level language, a function can have a deep hierarchy of operations. For big data, this can easily lead to inefficiency. Profiling is an important tool to understand the performance of an R code for both serial and parallel improvements.« less

A parallel 3-D discrete wavelet transform architecture using pipelined lifting scheme approach for video coding

NASA Astrophysics Data System (ADS)

Hegde, Ganapathi; Vaya, Pukhraj

2013-10-01

This article presents a parallel architecture for 3-D discrete wavelet transform (3-DDWT). The proposed design is based on the 1-D pipelined lifting scheme. The architecture is fully scalable beyond the present coherent Daubechies filter bank (9, 7). This 3-DDWT architecture has advantages such as no group of pictures restriction and reduced memory referencing. It offers low power consumption, low latency and high throughput. The computing technique is based on the concept that lifting scheme minimises the storage requirement. The application specific integrated circuit implementation of the proposed architecture is done by synthesising it using 65 nm Taiwan Semiconductor Manufacturing Company standard cell library. It offers a speed of 486 MHz with a power consumption of 2.56 mW. This architecture is suitable for real-time video compression even with large frame dimensions.

Chromium: A Stress-Processing Framework for Interactive Rendering on Clusters

DOE Office of Scientific and Technical Information (OSTI.GOV)

Humphreys, G,; Houston, M.; Ng, Y.-R.

2002-01-11

We describe Chromium, a system for manipulating streams of graphics API commands on clusters of workstations. Chromium's stream filters can be arranged to create sort-first and sort-last parallel graphics architectures that, in many cases, support the same applications while using only commodity graphics accelerators. In addition, these stream filters can be extended programmatically, allowing the user to customize the stream transformations performed by nodes in a cluster. Because our stream processing mechanism is completely general, any cluster-parallel rendering algorithm can be either implemented on top of or embedded in Chromium. In this paper, we give examples of real-world applications thatmore » use Chromium to achieve good scalability on clusters of workstations, and describe other potential uses of this stream processing technology. By completely abstracting the underlying graphics architecture, network topology, and API command processing semantics, we allow a variety of applications to run in different environments.« less

Large-scale parallel lattice Boltzmann-cellular automaton model of two-dimensional dendritic growth

NASA Astrophysics Data System (ADS)

Jelinek, Bohumir; Eshraghi, Mohsen; Felicelli, Sergio; Peters, John F.

2014-03-01

An extremely scalable lattice Boltzmann (LB)-cellular automaton (CA) model for simulations of two-dimensional (2D) dendritic solidification under forced convection is presented. The model incorporates effects of phase change, solute diffusion, melt convection, and heat transport. The LB model represents the diffusion, convection, and heat transfer phenomena. The dendrite growth is driven by a difference between actual and equilibrium liquid composition at the solid-liquid interface. The CA technique is deployed to track the new interface cells. The computer program was parallelized using the Message Passing Interface (MPI) technique. Parallel scaling of the algorithm was studied and major scalability bottlenecks were identified. Efficiency loss attributable to the high memory bandwidth requirement of the algorithm was observed when using multiple cores per processor. Parallel writing of the output variables of interest was implemented in the binary Hierarchical Data Format 5 (HDF5) to improve the output performance, and to simplify visualization. Calculations were carried out in single precision arithmetic without significant loss in accuracy, resulting in 50% reduction of memory and computational time requirements. The presented solidification model shows a very good scalability up to centimeter size domains, including more than ten million of dendrites. Catalogue identifier: AEQZ_v1_0 Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AEQZ_v1_0.html Program obtainable from: CPC Program Library, Queen’s University, Belfast, UK Licensing provisions: Standard CPC license, http://cpc.cs.qub.ac.uk/licence/licence.html No. of lines in distributed program, including test data, etc.: 29,767 No. of bytes in distributed program, including test data, etc.: 3131,367 Distribution format: tar.gz Programming language: Fortran 90. Computer: Linux PC and clusters. Operating system: Linux. Has the code been vectorized or parallelized?: Yes. Program is parallelized using MPI. Number of processors used: 1-50,000 RAM: Memory requirements depend on the grid size Classification: 6.5, 7.7. External routines: MPI (http://www.mcs.anl.gov/research/projects/mpi/), HDF5 (http://www.hdfgroup.org/HDF5/) Nature of problem: Dendritic growth in undercooled Al-3 wt% Cu alloy melt under forced convection. Solution method: The lattice Boltzmann model solves the diffusion, convection, and heat transfer phenomena. The cellular automaton technique is deployed to track the solid/liquid interface. Restrictions: Heat transfer is calculated uncoupled from the fluid flow. Thermal diffusivity is constant. Unusual features: Novel technique, utilizing periodic duplication of a pre-grown “incubation” domain, is applied for the scaleup test. Running time: Running time varies from minutes to days depending on the domain size and number of computational cores.

StagBL : A Scalable, Portable, High-Performance Discretization and Solver Layer for Geodynamic Simulation

NASA Astrophysics Data System (ADS)

Sanan, P.; Tackley, P. J.; Gerya, T.; Kaus, B. J. P.; May, D.

2017-12-01

StagBL is an open-source parallel solver and discretization library for geodynamic simulation,encapsulating and optimizing operations essential to staggered-grid finite volume Stokes flow solvers.It provides a parallel staggered-grid abstraction with a high-level interface in C and Fortran.On top of this abstraction, tools are available to define boundary conditions and interact with particle systems.Tools and examples to efficiently solve Stokes systems defined on the grid are provided in small (direct solver), medium (simple preconditioners), and large (block factorization and multigrid) model regimes.By working directly with leading application codes (StagYY, I3ELVIS, and LaMEM) and providing an API and examples to integrate with others, StagBL aims to become a community tool supplying scalable, portable, reproducible performance toward novel science in regional- and planet-scale geodynamics and planetary science.By implementing kernels used by many research groups beneath a uniform abstraction layer, the library will enable optimization for modern hardware, thus reducing community barriers to large- or extreme-scale parallel simulation on modern architectures. In particular, the library will include CPU-, Manycore-, and GPU-optimized variants of matrix-free operators and multigrid components.The common layer provides a framework upon which to introduce innovative new tools.StagBL will leverage p4est to provide distributed adaptive meshes, and incorporate a multigrid convergence analysis tool.These options, in addition to a wealth of solver options provided by an interface to PETSc, will make the most modern solution techniques available from a common interface. StagBL in turn provides a PETSc interface, DMStag, to its central staggered grid abstraction.We present public version 0.5 of StagBL, including preliminary integration with application codes and demonstrations with its own demonstration application, StagBLDemo. Central to StagBL is the notion of an uninterrupted pipeline from toy/teaching codes to high-performance, extreme-scale solves. StagBLDemo replicates the functionality of an advanced MATLAB-style regional geodynamics code, thus providing users with a concrete procedure to exceed the performance and scalability limitations of smaller-scale tools.

Symplectic multi-particle tracking on GPUs

NASA Astrophysics Data System (ADS)

Liu, Zhicong; Qiang, Ji

2018-05-01

A symplectic multi-particle tracking model is implemented on the Graphic Processing Units (GPUs) using the Compute Unified Device Architecture (CUDA) language. The symplectic tracking model can preserve phase space structure and reduce non-physical effects in long term simulation, which is important for beam property evaluation in particle accelerators. Though this model is computationally expensive, it is very suitable for parallelization and can be accelerated significantly by using GPUs. In this paper, we optimized the implementation of the symplectic tracking model on both single GPU and multiple GPUs. Using a single GPU processor, the code achieves a factor of 2-10 speedup for a range of problem sizes compared with the time on a single state-of-the-art Central Processing Unit (CPU) node with similar power consumption and semiconductor technology. It also shows good scalability on a multi-GPU cluster at Oak Ridge Leadership Computing Facility. In an application to beam dynamics simulation, the GPU implementation helps save more than a factor of two total computing time in comparison to the CPU implementation.

Accelerating k-NN Algorithm with Hybrid MPI and OpenSHMEM

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lin, Jian; Hamidouche, Khaled; Zheng, Jie

2015-08-05

Machine Learning algorithms are benefiting from the continuous improvement of programming models, including MPI, MapReduce and PGAS. k-Nearest Neighbors (k-NN) algorithm is a widely used machine learning algorithm, applied to supervised learning tasks such as classification. Several parallel implementations of k-NN have been proposed in the literature and practice. However, on high-performance computing systems with high-speed interconnects, it is important to further accelerate existing designs of the k-NN algorithm through taking advantage of scalable programming models. To improve the performance of k-NN on large-scale environment with InfiniBand network, this paper proposes several alternative hybrid MPI+OpenSHMEM designs and performs a systemicmore » evaluation and analysis on typical workloads. The hybrid designs leverage the one-sided memory access to better overlap communication with computation than the existing pure MPI design, and propose better schemes for efficient buffer management. The implementation based on k-NN program from MaTEx with MVAPICH2-X (Unified MPI+PGAS Communication Runtime over InfiniBand) shows up to 9.0% time reduction for training KDD Cup 2010 workload over 512 cores, and 27.6% time reduction for small workload with balanced communication and computation. Experiments of running with varied number of cores show that our design can maintain good scalability.« less

Lattice dynamics calculations based on density-functional perturbation theory in real space

NASA Astrophysics Data System (ADS)

Shang, Honghui; Carbogno, Christian; Rinke, Patrick; Scheffler, Matthias

2017-06-01

A real-space formalism for density-functional perturbation theory (DFPT) is derived and applied for the computation of harmonic vibrational properties in molecules and solids. The practical implementation using numeric atom-centered orbitals as basis functions is demonstrated exemplarily for the all-electron Fritz Haber Institute ab initio molecular simulations (FHI-aims) package. The convergence of the calculations with respect to numerical parameters is carefully investigated and a systematic comparison with finite-difference approaches is performed both for finite (molecules) and extended (periodic) systems. Finally, the scaling tests and scalability tests on massively parallel computer systems demonstrate the computational efficiency.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Rizzi, Silvio; Hereld, Mark; Insley, Joseph

In this work we perform in-situ visualization of molecular dynamics simulations, which can help scientists to visualize simulation output on-the-fly, without incurring storage overheads. We present a case study to couple LAMMPS, the large-scale molecular dynamics simulation code with vl3, our parallel framework for large-scale visualization and analysis. Our motivation is to identify effective approaches for covisualization and exploration of large-scale atomistic simulations at interactive frame rates.We propose a system of coupled libraries and describe its architecture, with an implementation that runs on GPU-based clusters. We present the results of strong and weak scalability experiments, as well as future researchmore » avenues based on our results.« less

Numerical characteristics of quantum computer simulation

NASA Astrophysics Data System (ADS)

Chernyavskiy, A.; Khamitov, K.; Teplov, A.; Voevodin, V.; Voevodin, Vl.

2016-12-01

The simulation of quantum circuits is significantly important for the implementation of quantum information technologies. The main difficulty of such modeling is the exponential growth of dimensionality, thus the usage of modern high-performance parallel computations is relevant. As it is well known, arbitrary quantum computation in circuit model can be done by only single- and two-qubit gates, and we analyze the computational structure and properties of the simulation of such gates. We investigate the fact that the unique properties of quantum nature lead to the computational properties of the considered algorithms: the quantum parallelism make the simulation of quantum gates highly parallel, and on the other hand, quantum entanglement leads to the problem of computational locality during simulation. We use the methodology of the AlgoWiki project (algowiki-project.org) to analyze the algorithm. This methodology consists of theoretical (sequential and parallel complexity, macro structure, and visual informational graph) and experimental (locality and memory access, scalability and more specific dynamic characteristics) parts. Experimental part was made by using the petascale Lomonosov supercomputer (Moscow State University, Russia). We show that the simulation of quantum gates is a good base for the research and testing of the development methods for data intense parallel software, and considered methodology of the analysis can be successfully used for the improvement of the algorithms in quantum information science.

Hierarchical Petascale Simulation Framework For Stress Corrosion Cracking

DOE Office of Scientific and Technical Information (OSTI.GOV)

Grama, Ananth

2013-12-18

A number of major accomplishments resulted from the project. These include: • Data Structures, Algorithms, and Numerical Methods for Reactive Molecular Dynamics. We have developed a range of novel data structures, algorithms, and solvers (amortized ILU, Spike) for use with ReaxFF and charge equilibration. • Parallel Formulations of ReactiveMD (Purdue ReactiveMolecular Dynamics Package, PuReMD, PuReMD-GPU, and PG-PuReMD) for Messaging, GPU, and GPU Cluster Platforms. We have developed efficient serial, parallel (MPI), GPU (Cuda), and GPU Cluster (MPI/Cuda) implementations. Our implementations have been demonstrated to be significantly better than the state of the art, both in terms of performance and scalability.more » • Comprehensive Validation in the Context of Diverse Applications. We have demonstrated the use of our software in diverse systems, including silica-water, silicon-germanium nanorods, and as part of other projects, extended it to applications ranging from explosives (RDX) to lipid bilayers (biomembranes under oxidative stress). • Open Source Software Packages for Reactive Molecular Dynamics. All versions of our soft- ware have been released over the public domain. There are over 100 major research groups worldwide using our software. • Implementation into the Department of Energy LAMMPS Software Package. We have also integrated our software into the Department of Energy LAMMPS software package.« less

Modern gyrokinetic particle-in-cell simulation of fusion plasmas on top supercomputers

DOE PAGES

Wang, Bei; Ethier, Stephane; Tang, William; ...

2017-06-29

The Gyrokinetic Toroidal Code at Princeton (GTC-P) is a highly scalable and portable particle-in-cell (PIC) code. It solves the 5D Vlasov-Poisson equation featuring efficient utilization of modern parallel computer architectures at the petascale and beyond. Motivated by the goal of developing a modern code capable of dealing with the physics challenge of increasing problem size with sufficient resolution, new thread-level optimizations have been introduced as well as a key additional domain decomposition. GTC-P's multiple levels of parallelism, including inter-node 2D domain decomposition and particle decomposition, as well as intra-node shared memory partition and vectorization have enabled pushing the scalability ofmore » the PIC method to extreme computational scales. In this paper, we describe the methods developed to build a highly parallelized PIC code across a broad range of supercomputer designs. This particularly includes implementations on heterogeneous systems using NVIDIA GPU accelerators and Intel Xeon Phi (MIC) co-processors and performance comparisons with state-of-the-art homogeneous HPC systems such as Blue Gene/Q. New discovery science capabilities in the magnetic fusion energy application domain are enabled, including investigations of Ion-Temperature-Gradient (ITG) driven turbulence simulations with unprecedented spatial resolution and long temporal duration. Performance studies with realistic fusion experimental parameters are carried out on multiple supercomputing systems spanning a wide range of cache capacities, cache-sharing configurations, memory bandwidth, interconnects and network topologies. These performance comparisons using a realistic discovery-science-capable domain application code provide valuable insights on optimization techniques across one of the broadest sets of current high-end computing platforms worldwide.« less

Modern gyrokinetic particle-in-cell simulation of fusion plasmas on top supercomputers

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wang, Bei; Ethier, Stephane; Tang, William

The Gyrokinetic Toroidal Code at Princeton (GTC-P) is a highly scalable and portable particle-in-cell (PIC) code. It solves the 5D Vlasov-Poisson equation featuring efficient utilization of modern parallel computer architectures at the petascale and beyond. Motivated by the goal of developing a modern code capable of dealing with the physics challenge of increasing problem size with sufficient resolution, new thread-level optimizations have been introduced as well as a key additional domain decomposition. GTC-P's multiple levels of parallelism, including inter-node 2D domain decomposition and particle decomposition, as well as intra-node shared memory partition and vectorization have enabled pushing the scalability ofmore » the PIC method to extreme computational scales. In this paper, we describe the methods developed to build a highly parallelized PIC code across a broad range of supercomputer designs. This particularly includes implementations on heterogeneous systems using NVIDIA GPU accelerators and Intel Xeon Phi (MIC) co-processors and performance comparisons with state-of-the-art homogeneous HPC systems such as Blue Gene/Q. New discovery science capabilities in the magnetic fusion energy application domain are enabled, including investigations of Ion-Temperature-Gradient (ITG) driven turbulence simulations with unprecedented spatial resolution and long temporal duration. Performance studies with realistic fusion experimental parameters are carried out on multiple supercomputing systems spanning a wide range of cache capacities, cache-sharing configurations, memory bandwidth, interconnects and network topologies. These performance comparisons using a realistic discovery-science-capable domain application code provide valuable insights on optimization techniques across one of the broadest sets of current high-end computing platforms worldwide.« less

Distributed Parallel Processing and Dynamic Load Balancing Techniques for Multidisciplinary High Speed Aircraft Design

NASA Technical Reports Server (NTRS)

Krasteva, Denitza T.

1998-01-01

Multidisciplinary design optimization (MDO) for large-scale engineering problems poses many challenges (e.g., the design of an efficient concurrent paradigm for global optimization based on disciplinary analyses, expensive computations over vast data sets, etc.) This work focuses on the application of distributed schemes for massively parallel architectures to MDO problems, as a tool for reducing computation time and solving larger problems. The specific problem considered here is configuration optimization of a high speed civil transport (HSCT), and the efficient parallelization of the embedded paradigm for reasonable design space identification. Two distributed dynamic load balancing techniques (random polling and global round robin with message combining) and two necessary termination detection schemes (global task count and token passing) were implemented and evaluated in terms of effectiveness and scalability to large problem sizes and a thousand processors. The effect of certain parameters on execution time was also inspected. Empirical results demonstrated stable performance and effectiveness for all schemes, and the parametric study showed that the selected algorithmic parameters have a negligible effect on performance.

Parallel numerical modeling of hybrid-dimensional compositional non-isothermal Darcy flows in fractured porous media

NASA Astrophysics Data System (ADS)

Xing, F.; Masson, R.; Lopez, S.

2017-09-01

This paper introduces a new discrete fracture model accounting for non-isothermal compositional multiphase Darcy flows and complex networks of fractures with intersecting, immersed and non-immersed fractures. The so called hybrid-dimensional model using a 2D model in the fractures coupled with a 3D model in the matrix is first derived rigorously starting from the equi-dimensional matrix fracture model. Then, it is discretized using a fully implicit time integration combined with the Vertex Approximate Gradient (VAG) finite volume scheme which is adapted to polyhedral meshes and anisotropic heterogeneous media. The fully coupled systems are assembled and solved in parallel using the Single Program Multiple Data (SPMD) paradigm with one layer of ghost cells. This strategy allows for a local assembly of the discrete systems. An efficient preconditioner is implemented to solve the linear systems at each time step and each Newton type iteration of the simulation. The numerical efficiency of our approach is assessed on different meshes, fracture networks, and physical settings in terms of parallel scalability, nonlinear convergence and linear convergence.

Message Passing and Shared Address Space Parallelism on an SMP Cluster

NASA Technical Reports Server (NTRS)

Shan, Hongzhang; Singh, Jaswinder P.; Oliker, Leonid; Biswas, Rupak; Biegel, Bryan (Technical Monitor)

2002-01-01

Currently, message passing (MP) and shared address space (SAS) are the two leading parallel programming paradigms. MP has been standardized with MPI, and is the more common and mature approach; however, code development can be extremely difficult, especially for irregularly structured computations. SAS offers substantial ease of programming, but may suffer from performance limitations due to poor spatial locality and high protocol overhead. In this paper, we compare the performance of and the programming effort required for six applications under both programming models on a 32-processor PC-SMP cluster, a platform that is becoming increasingly attractive for high-end scientific computing. Our application suite consists of codes that typically do not exhibit scalable performance under shared-memory programming due to their high communication-to-computation ratios and/or complex communication patterns. Results indicate that SAS can achieve about half the parallel efficiency of MPI for most of our applications, while being competitive for the others. A hybrid MPI+SAS strategy shows only a small performance advantage over pure MPI in some cases. Finally, improved implementations of two MPI collective operations on PC-SMP clusters are presented.

Evaluation of concurrent priority queue algorithms. Technical report

DOE Office of Scientific and Technical Information (OSTI.GOV)

Huang, Q.

1991-02-01

The priority queue is a fundamental data structure that is used in a large variety of parallel algorithms, such as multiprocessor scheduling and parallel best-first search of state-space graphs. This thesis addresses the design and experimental evaluation of two novel concurrent priority queues: a parallel Fibonacci heap and a concurrent priority pool, and compares them with the concurrent binary heap. The parallel Fibonacci heap is based on the sequential Fibonacci heap, which is theoretically the most efficient data structure for sequential priority queues. This scheme not only preserves the efficient operation time bounds of its sequential counterpart, but also hasmore » very low contention by distributing locks over the entire data structure. The experimental results show its linearly scalable throughput and speedup up to as many processors as tested (currently 18). A concurrent access scheme for a doubly linked list is described as part of the implementation of the parallel Fibonacci heap. The concurrent priority pool is based on the concurrent B-tree and the concurrent pool. The concurrent priority pool has the highest throughput among the priority queues studied. Like the parallel Fibonacci heap, the concurrent priority pool scales linearly up to as many processors as tested. The priority queues are evaluated in terms of throughput and speedup. Some applications of concurrent priority queues such as the vertex cover problem and the single source shortest path problem are tested.« less

An asymptotic-preserving Lagrangian algorithm for the time-dependent anisotropic heat transport equation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chacon, Luis; del-Castillo-Negrete, Diego; Hauck, Cory D.

2014-09-01

We propose a Lagrangian numerical algorithm for a time-dependent, anisotropic temperature transport equation in magnetized plasmas in the large guide field regime. The approach is based on an analytical integral formal solution of the parallel (i.e., along the magnetic field) transport equation with sources, and it is able to accommodate both local and non-local parallel heat flux closures. The numerical implementation is based on an operator-split formulation, with two straightforward steps: a perpendicular transport step (including sources), and a Lagrangian (field-line integral) parallel transport step. Algorithmically, the first step is amenable to the use of modern iterative methods, while themore » second step has a fixed cost per degree of freedom (and is therefore scalable). Accuracy-wise, the approach is free from the numerical pollution introduced by the discrete parallel transport term when the perpendicular to parallel transport coefficient ratio X ⊥ /X ∥ becomes arbitrarily small, and is shown to capture the correct limiting solution when ε = X⊥L 2 ∥/X1L 2 ⊥ → 0 (with L∥∙ L⊥ , the parallel and perpendicular diffusion length scales, respectively). Therefore, the approach is asymptotic-preserving. We demonstrate the capabilities of the scheme with several numerical experiments with varying magnetic field complexity in two dimensions, including the case of transport across a magnetic island.« less

Highly scalable parallel processing of extracellular recordings of Multielectrode Arrays.

PubMed

Gehring, Tiago V; Vasilaki, Eleni; Giugliano, Michele

2015-01-01

Technological advances of Multielectrode Arrays (MEAs) used for multisite, parallel electrophysiological recordings, lead to an ever increasing amount of raw data being generated. Arrays with hundreds up to a few thousands of electrodes are slowly seeing widespread use and the expectation is that more sophisticated arrays will become available in the near future. In order to process the large data volumes resulting from MEA recordings there is a pressing need for new software tools able to process many data channels in parallel. Here we present a new tool for processing MEA data recordings that makes use of new programming paradigms and recent technology developments to unleash the power of modern highly parallel hardware, such as multi-core CPUs with vector instruction sets or GPGPUs. Our tool builds on and complements existing MEA data analysis packages. It shows high scalability and can be used to speed up some performance critical pre-processing steps such as data filtering and spike detection, helping to make the analysis of larger data sets tractable.

Evaluating the performance of parallel subsurface simulators: An illustrative example with PFLOTRAN

PubMed Central

Hammond, G E; Lichtner, P C; Mills, R T

2014-01-01

[1] To better inform the subsurface scientist on the expected performance of parallel simulators, this work investigates performance of the reactive multiphase flow and multicomponent biogeochemical transport code PFLOTRAN as it is applied to several realistic modeling scenarios run on the Jaguar supercomputer. After a brief introduction to the code's parallel layout and code design, PFLOTRAN's parallel performance (measured through strong and weak scalability analyses) is evaluated in the context of conceptual model layout, software and algorithmic design, and known hardware limitations. PFLOTRAN scales well (with regard to strong scaling) for three realistic problem scenarios: (1) in situ leaching of copper from a mineral ore deposit within a 5-spot flow regime, (2) transient flow and solute transport within a regional doublet, and (3) a real-world problem involving uranium surface complexation within a heterogeneous and extremely dynamic variably saturated flow field. Weak scalability is discussed in detail for the regional doublet problem, and several difficulties with its interpretation are noted. PMID:25506097

Evaluating the performance of parallel subsurface simulators: An illustrative example with PFLOTRAN.

PubMed

Hammond, G E; Lichtner, P C; Mills, R T

2014-01-01

[1] To better inform the subsurface scientist on the expected performance of parallel simulators, this work investigates performance of the reactive multiphase flow and multicomponent biogeochemical transport code PFLOTRAN as it is applied to several realistic modeling scenarios run on the Jaguar supercomputer. After a brief introduction to the code's parallel layout and code design, PFLOTRAN's parallel performance (measured through strong and weak scalability analyses) is evaluated in the context of conceptual model layout, software and algorithmic design, and known hardware limitations. PFLOTRAN scales well (with regard to strong scaling) for three realistic problem scenarios: (1) in situ leaching of copper from a mineral ore deposit within a 5-spot flow regime, (2) transient flow and solute transport within a regional doublet, and (3) a real-world problem involving uranium surface complexation within a heterogeneous and extremely dynamic variably saturated flow field. Weak scalability is discussed in detail for the regional doublet problem, and several difficulties with its interpretation are noted.

Implementation and Testing of VLBI Software Correlation at the USNO

NASA Technical Reports Server (NTRS)

Fey, Alan; Ojha, Roopesh; Boboltz, Dave; Geiger, Nicole; Kingham, Kerry; Hall, David; Gaume, Ralph; Johnston, Ken

2010-01-01

The Washington Correlator (WACO) at the U.S. Naval Observatory (USNO) is a dedicated VLBI processor based on dedicated hardware of ASIC design. The WACO is currently over 10 years old and is nearing the end of its expected lifetime. Plans for implementation and testing of software correlation at the USNO are currently being considered. The VLBI correlation process is, by its very nature, well suited to a parallelized computing environment. Commercial off-the-shelf computer hardware has advanced in processing power to the point where software correlation is now both economically and technologically feasible. The advantages of software correlation are manifold but include flexibility, scalability, and easy adaptability to changing environments and requirements. We discuss our experience with and plans for use of software correlation at USNO with emphasis on the use of the DiFX software correlator.

Cpu/gpu Computing for AN Implicit Multi-Block Compressible Navier-Stokes Solver on Heterogeneous Platform

NASA Astrophysics Data System (ADS)

Deng, Liang; Bai, Hanli; Wang, Fang; Xu, Qingxin

2016-06-01

CPU/GPU computing allows scientists to tremendously accelerate their numerical codes. In this paper, we port and optimize a double precision alternating direction implicit (ADI) solver for three-dimensional compressible Navier-Stokes equations from our in-house Computational Fluid Dynamics (CFD) software on heterogeneous platform. First, we implement a full GPU version of the ADI solver to remove a lot of redundant data transfers between CPU and GPU, and then design two fine-grain schemes, namely “one-thread-one-point” and “one-thread-one-line”, to maximize the performance. Second, we present a dual-level parallelization scheme using the CPU/GPU collaborative model to exploit the computational resources of both multi-core CPUs and many-core GPUs within the heterogeneous platform. Finally, considering the fact that memory on a single node becomes inadequate when the simulation size grows, we present a tri-level hybrid programming pattern MPI-OpenMP-CUDA that merges fine-grain parallelism using OpenMP and CUDA threads with coarse-grain parallelism using MPI for inter-node communication. We also propose a strategy to overlap the computation with communication using the advanced features of CUDA and MPI programming. We obtain speedups of 6.0 for the ADI solver on one Tesla M2050 GPU in contrast to two Xeon X5670 CPUs. Scalability tests show that our implementation can offer significant performance improvement on heterogeneous platform.

Automation of multi-agent control for complex dynamic systems in heterogeneous computational network

NASA Astrophysics Data System (ADS)

Oparin, Gennady; Feoktistov, Alexander; Bogdanova, Vera; Sidorov, Ivan

2017-01-01

The rapid progress of high-performance computing entails new challenges related to solving large scientific problems for various subject domains in a heterogeneous distributed computing environment (e.g., a network, Grid system, or Cloud infrastructure). The specialists in the field of parallel and distributed computing give the special attention to a scalability of applications for problem solving. An effective management of the scalable application in the heterogeneous distributed computing environment is still a non-trivial issue. Control systems that operate in networks, especially relate to this issue. We propose a new approach to the multi-agent management for the scalable applications in the heterogeneous computational network. The fundamentals of our approach are the integrated use of conceptual programming, simulation modeling, network monitoring, multi-agent management, and service-oriented programming. We developed a special framework for an automation of the problem solving. Advantages of the proposed approach are demonstrated on the parametric synthesis example of the static linear regulator for complex dynamic systems. Benefits of the scalable application for solving this problem include automation of the multi-agent control for the systems in a parallel mode with various degrees of its detailed elaboration.

Scalable splitting algorithms for big-data interferometric imaging in the SKA era

NASA Astrophysics Data System (ADS)

Onose, Alexandru; Carrillo, Rafael E.; Repetti, Audrey; McEwen, Jason D.; Thiran, Jean-Philippe; Pesquet, Jean-Christophe; Wiaux, Yves

2016-11-01

In the context of next-generation radio telescopes, like the Square Kilometre Array (SKA), the efficient processing of large-scale data sets is extremely important. Convex optimization tasks under the compressive sensing framework have recently emerged and provide both enhanced image reconstruction quality and scalability to increasingly larger data sets. We focus herein mainly on scalability and propose two new convex optimization algorithmic structures able to solve the convex optimization tasks arising in radio-interferometric imaging. They rely on proximal splitting and forward-backward iterations and can be seen, by analogy, with the CLEAN major-minor cycle, as running sophisticated CLEAN-like iterations in parallel in multiple data, prior, and image spaces. Both methods support any convex regularization function, in particular, the well-studied ℓ1 priors promoting image sparsity in an adequate domain. Tailored for big-data, they employ parallel and distributed computations to achieve scalability, in terms of memory and computational requirements. One of them also exploits randomization, over data blocks at each iteration, offering further flexibility. We present simulation results showing the feasibility of the proposed methods as well as their advantages compared to state-of-the-art algorithmic solvers. Our MATLAB code is available online on GitHub.

Optimized Hypervisor Scheduler for Parallel Discrete Event Simulations on Virtual Machine Platforms

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yoginath, Srikanth B; Perumalla, Kalyan S

2013-01-01

With the advent of virtual machine (VM)-based platforms for parallel computing, it is now possible to execute parallel discrete event simulations (PDES) over multiple virtual machines, in contrast to executing in native mode directly over hardware as is traditionally done over the past decades. While mature VM-based parallel systems now offer new, compelling benefits such as serviceability, dynamic reconfigurability and overall cost effectiveness, the runtime performance of parallel applications can be significantly affected. In particular, most VM-based platforms are optimized for general workloads, but PDES execution exhibits unique dynamics significantly different from other workloads. Here we first present results frommore » experiments that highlight the gross deterioration of the runtime performance of VM-based PDES simulations when executed using traditional VM schedulers, quantitatively showing the bad scaling properties of the scheduler as the number of VMs is increased. The mismatch is fundamental in nature in the sense that any fairness-based VM scheduler implementation would exhibit this mismatch with PDES runs. We also present a new scheduler optimized specifically for PDES applications, and describe its design and implementation. Experimental results obtained from running PDES benchmarks (PHOLD and vehicular traffic simulations) over VMs show over an order of magnitude improvement in the run time of the PDES-optimized scheduler relative to the regular VM scheduler, with over 20 reduction in run time of simulations using up to 64 VMs. The observations and results are timely in the context of emerging systems such as cloud platforms and VM-based high performance computing installations, highlighting to the community the need for PDES-specific support, and the feasibility of significantly reducing the runtime overhead for scalable PDES on VM platforms.« less

An Energy-Efficient and Scalable Deep Learning/Inference Processor With Tetra-Parallel MIMD Architecture for Big Data Applications.

PubMed

Park, Seong-Wook; Park, Junyoung; Bong, Kyeongryeol; Shin, Dongjoo; Lee, Jinmook; Choi, Sungpill; Yoo, Hoi-Jun

2015-12-01

Deep Learning algorithm is widely used for various pattern recognition applications such as text recognition, object recognition and action recognition because of its best-in-class recognition accuracy compared to hand-crafted algorithm and shallow learning based algorithms. Long learning time caused by its complex structure, however, limits its usage only in high-cost servers or many-core GPU platforms so far. On the other hand, the demand on customized pattern recognition within personal devices will grow gradually as more deep learning applications will be developed. This paper presents a SoC implementation to enable deep learning applications to run with low cost platforms such as mobile or portable devices. Different from conventional works which have adopted massively-parallel architecture, this work adopts task-flexible architecture and exploits multiple parallelism to cover complex functions of convolutional deep belief network which is one of popular deep learning/inference algorithms. In this paper, we implement the most energy-efficient deep learning and inference processor for wearable system. The implemented 2.5 mm × 4.0 mm deep learning/inference processor is fabricated using 65 nm 8-metal CMOS technology for a battery-powered platform with real-time deep inference and deep learning operation. It consumes 185 mW average power, and 213.1 mW peak power at 200 MHz operating frequency and 1.2 V supply voltage. It achieves 411.3 GOPS peak performance and 1.93 TOPS/W energy efficiency, which is 2.07× higher than the state-of-the-art.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Biyikli, Emre; To, Albert C., E-mail: albertto@pitt.edu

Atomistic/continuum coupling methods combine accurate atomistic methods and efficient continuum methods to simulate the behavior of highly ordered crystalline systems. Coupled methods utilize the advantages of both approaches to simulate systems at a lower computational cost, while retaining the accuracy associated with atomistic methods. Many concurrent atomistic/continuum coupling methods have been proposed in the past; however, their true computational efficiency has not been demonstrated. The present work presents an efficient implementation of a concurrent coupling method called the Multiresolution Molecular Mechanics (MMM) for serial, parallel, and adaptive analysis. First, we present the features of the software implemented along with themore » associated technologies. The scalability of the software implementation is demonstrated, and the competing effects of multiscale modeling and parallelization are discussed. Then, the algorithms contributing to the efficiency of the software are presented. These include algorithms for eliminating latent ghost atoms from calculations and measurement-based dynamic balancing of parallel workload. The efficiency improvements made by these algorithms are demonstrated by benchmark tests. The efficiency of the software is found to be on par with LAMMPS, a state-of-the-art Molecular Dynamics (MD) simulation code, when performing full atomistic simulations. Speed-up of the MMM method is shown to be directly proportional to the reduction of the number of the atoms visited in force computation. Finally, an adaptive MMM analysis on a nanoindentation problem, containing over a million atoms, is performed, yielding an improvement of 6.3–8.5 times in efficiency, over the full atomistic MD method. For the first time, the efficiency of a concurrent atomistic/continuum coupling method is comprehensively investigated and demonstrated.« less

A domain specific language for performance portable molecular dynamics algorithms

NASA Astrophysics Data System (ADS)

Saunders, William Robert; Grant, James; Müller, Eike Hermann

2018-03-01

Developers of Molecular Dynamics (MD) codes face significant challenges when adapting existing simulation packages to new hardware. In a continuously diversifying hardware landscape it becomes increasingly difficult for scientists to be experts both in their own domain (physics/chemistry/biology) and specialists in the low level parallelisation and optimisation of their codes. To address this challenge, we describe a "Separation of Concerns" approach for the development of parallel and optimised MD codes: the science specialist writes code at a high abstraction level in a domain specific language (DSL), which is then translated into efficient computer code by a scientific programmer. In a related context, an abstraction for the solution of partial differential equations with grid based methods has recently been implemented in the (Py)OP2 library. Inspired by this approach, we develop a Python code generation system for molecular dynamics simulations on different parallel architectures, including massively parallel distributed memory systems and GPUs. We demonstrate the efficiency of the auto-generated code by studying its performance and scalability on different hardware and compare it to other state-of-the-art simulation packages. With growing data volumes the extraction of physically meaningful information from the simulation becomes increasingly challenging and requires equally efficient implementations. A particular advantage of our approach is the easy expression of such analysis algorithms. We consider two popular methods for deducing the crystalline structure of a material from the local environment of each atom, show how they can be expressed in our abstraction and implement them in the code generation framework.

Multiple Independent File Parallel I/O with HDF5

DOE Office of Scientific and Technical Information (OSTI.GOV)

Miller, M. C.

2016-07-13

The HDF5 library has supported the I/O requirements of HPC codes at Lawrence Livermore National Labs (LLNL) since the late 90’s. In particular, HDF5 used in the Multiple Independent File (MIF) parallel I/O paradigm has supported LLNL code’s scalable I/O requirements and has recently been gainfully used at scales as large as O(10 6) parallel tasks.

A Parallel Relational Database Management System Approach to Relevance Feedback in Information Retrieval.

ERIC Educational Resources Information Center

Lundquist, Carol; Frieder, Ophir; Holmes, David O.; Grossman, David

1999-01-01

Describes a scalable, parallel, relational database-drive information retrieval engine. To support portability across a wide range of execution environments, all algorithms adhere to the SQL-92 standard. By incorporating relevance feedback algorithms, accuracy is enhanced over prior database-driven information retrieval efforts. Presents…

DOE Office of Scientific and Technical Information (OSTI.GOV)

Barbara Chapman

OpenMP was not well recognized at the beginning of the project, around year 2003, because of its limited use in DoE production applications and the inmature hardware support for an efficient implementation. Yet in the recent years, it has been graduately adopted both in HPC applications, mostly in the form of MPI+OpenMP hybrid code, and in mid-scale desktop applications for scientific and experimental studies. We have observed this trend and worked deligiently to improve our OpenMP compiler and runtimes, as well as to work with the OpenMP standard organization to make sure OpenMP are evolved in the direction close tomore » DoE missions. In the Center for Programming Models for Scalable Parallel Computing project, the HPCTools team at the University of Houston (UH), directed by Dr. Barbara Chapman, has been working with project partners, external collaborators and hardware vendors to increase the scalability and applicability of OpenMP for multi-core (and future manycore) platforms and for distributed memory systems by exploring different programming models, language extensions, compiler optimizations, as well as runtime library support.« less

Polarizable Molecular Dynamics in a Polarizable Continuum Solvent

PubMed Central

Lipparini, Filippo; Lagardère, Louis; Raynaud, Christophe; Stamm, Benjamin; Cancès, Eric; Mennucci, Benedetta; Schnieders, Michael; Ren, Pengyu; Maday, Yvon; Piquemal, Jean-Philip

2015-01-01

We present for the first time scalable polarizable molecular dynamics (MD) simulations within a polarizable continuum solvent with molecular shape cavities and exact solution of the mutual polarization. The key ingredients are a very efficient algorithm for solving the equations associated with the polarizable continuum, in particular, the domain decomposition Conductor-like Screening Model (ddCOSMO), a rigorous coupling of the continuum with the polarizable force field achieved through a robust variational formulation and an effective strategy to solve the coupled equations. The coupling of ddCOSMO with non variational force fields, including AMOEBA, is also addressed. The MD simulations are feasible, for real life systems, on standard cluster nodes; a scalable parallel implementation allows for further speed up in the context of a newly developed module in Tinker, named Tinker-HP. NVE simulations are stable and long term energy conservation can be achieved. This paper is focused on the methodological developments, on the analysis of the algorithm and on the stability of the simulations; a proof-of-concept application is also presented to attest the possibilities of this newly developed technique. PMID:26516318

The Open Connectome Project Data Cluster: Scalable Analysis and Vision for High-Throughput Neuroscience.

PubMed

Burns, Randal; Roncal, William Gray; Kleissas, Dean; Lillaney, Kunal; Manavalan, Priya; Perlman, Eric; Berger, Daniel R; Bock, Davi D; Chung, Kwanghun; Grosenick, Logan; Kasthuri, Narayanan; Weiler, Nicholas C; Deisseroth, Karl; Kazhdan, Michael; Lichtman, Jeff; Reid, R Clay; Smith, Stephen J; Szalay, Alexander S; Vogelstein, Joshua T; Vogelstein, R Jacob

2013-01-01

We describe a scalable database cluster for the spatial analysis and annotation of high-throughput brain imaging data, initially for 3-d electron microscopy image stacks, but for time-series and multi-channel data as well. The system was designed primarily for workloads that build connectomes - neural connectivity maps of the brain-using the parallel execution of computer vision algorithms on high-performance compute clusters. These services and open-science data sets are publicly available at openconnecto.me. The system design inherits much from NoSQL scale-out and data-intensive computing architectures. We distribute data to cluster nodes by partitioning a spatial index. We direct I/O to different systems-reads to parallel disk arrays and writes to solid-state storage-to avoid I/O interference and maximize throughput. All programming interfaces are RESTful Web services, which are simple and stateless, improving scalability and usability. We include a performance evaluation of the production system, highlighting the effec-tiveness of spatial data organization.

The Open Connectome Project Data Cluster: Scalable Analysis and Vision for High-Throughput Neuroscience

PubMed Central

Burns, Randal; Roncal, William Gray; Kleissas, Dean; Lillaney, Kunal; Manavalan, Priya; Perlman, Eric; Berger, Daniel R.; Bock, Davi D.; Chung, Kwanghun; Grosenick, Logan; Kasthuri, Narayanan; Weiler, Nicholas C.; Deisseroth, Karl; Kazhdan, Michael; Lichtman, Jeff; Reid, R. Clay; Smith, Stephen J.; Szalay, Alexander S.; Vogelstein, Joshua T.; Vogelstein, R. Jacob

2013-01-01

We describe a scalable database cluster for the spatial analysis and annotation of high-throughput brain imaging data, initially for 3-d electron microscopy image stacks, but for time-series and multi-channel data as well. The system was designed primarily for workloads that build connectomes— neural connectivity maps of the brain—using the parallel execution of computer vision algorithms on high-performance compute clusters. These services and open-science data sets are publicly available at openconnecto.me. The system design inherits much from NoSQL scale-out and data-intensive computing architectures. We distribute data to cluster nodes by partitioning a spatial index. We direct I/O to different systems—reads to parallel disk arrays and writes to solid-state storage—to avoid I/O interference and maximize throughput. All programming interfaces are RESTful Web services, which are simple and stateless, improving scalability and usability. We include a performance evaluation of the production system, highlighting the effec-tiveness of spatial data organization. PMID:24401992

Scalable Parallel Computation for Extended MHD Modeling of Fusion Plasmas

NASA Astrophysics Data System (ADS)

Glasser, Alan H.

2008-11-01

Parallel solution of a linear system is scalable if simultaneously doubling the number of dependent variables and the number of processors results in little or no increase in the computation time to solution. Two approaches have this property for parabolic systems: multigrid and domain decomposition. Since extended MHD is primarily a hyperbolic rather than a parabolic system, additional steps must be taken to parabolize the linear system to be solved by such a method. Such physics-based preconditioning (PBP) methods have been pioneered by Chac'on, using finite volumes for spatial discretization, multigrid for solution of the preconditioning equations, and matrix-free Newton-Krylov methods for the accurate solution of the full nonlinear preconditioned equations. The work described here is an extension of these methods using high-order spectral element methods and FETI-DP domain decomposition. Application of PBP to a flux-source representation of the physics equations is discussed. The resulting scalability will be demonstrated for simple wave and for ideal and Hall MHD waves.

ICON-MIC: Implementing a CPU/MIC Collaboration Parallel Framework for ICON on Tianhe-2 Supercomputer.

PubMed

Wang, Zihao; Chen, Yu; Zhang, Jingrong; Li, Lun; Wan, Xiaohua; Liu, Zhiyong; Sun, Fei; Zhang, Fa

2018-03-01

Electron tomography (ET) is an important technique for studying the three-dimensional structures of the biological ultrastructure. Recently, ET has reached sub-nanometer resolution for investigating the native and conformational dynamics of macromolecular complexes by combining with the sub-tomogram averaging approach. Due to the limited sampling angles, ET reconstruction typically suffers from the "missing wedge" problem. Using a validation procedure, iterative compressed-sensing optimized nonuniform fast Fourier transform (NUFFT) reconstruction (ICON) demonstrates its power in restoring validated missing information for a low-signal-to-noise ratio biological ET dataset. However, the huge computational demand has become a bottleneck for the application of ICON. In this work, we implemented a parallel acceleration technology ICON-many integrated core (MIC) on Xeon Phi cards to address the huge computational demand of ICON. During this step, we parallelize the element-wise matrix operations and use the efficient summation of a matrix to reduce the cost of matrix computation. We also developed parallel versions of NUFFT on MIC to achieve a high acceleration of ICON by using more efficient fast Fourier transform (FFT) calculation. We then proposed a hybrid task allocation strategy (two-level load balancing) to improve the overall performance of ICON-MIC by making full use of the idle resources on Tianhe-2 supercomputer. Experimental results using two different datasets show that ICON-MIC has high accuracy in biological specimens under different noise levels and a significant acceleration, up to 13.3 × , compared with the CPU version. Further, ICON-MIC has good scalability efficiency and overall performance on Tianhe-2 supercomputer.

An FPGA-Based Massively Parallel Neuromorphic Cortex Simulator

PubMed Central

Wang, Runchun M.; Thakur, Chetan S.; van Schaik, André

2018-01-01

This paper presents a massively parallel and scalable neuromorphic cortex simulator designed for simulating large and structurally connected spiking neural networks, such as complex models of various areas of the cortex. The main novelty of this work is the abstraction of a neuromorphic architecture into clusters represented by minicolumns and hypercolumns, analogously to the fundamental structural units observed in neurobiology. Without this approach, simulating large-scale fully connected networks needs prohibitively large memory to store look-up tables for point-to-point connections. Instead, we use a novel architecture, based on the structural connectivity in the neocortex, such that all the required parameters and connections can be stored in on-chip memory. The cortex simulator can be easily reconfigured for simulating different neural networks without any change in hardware structure by programming the memory. A hierarchical communication scheme allows one neuron to have a fan-out of up to 200 k neurons. As a proof-of-concept, an implementation on one Altera Stratix V FPGA was able to simulate 20 million to 2.6 billion leaky-integrate-and-fire (LIF) neurons in real time. We verified the system by emulating a simplified auditory cortex (with 100 million neurons). This cortex simulator achieved a low power dissipation of 1.62 μW per neuron. With the advent of commercially available FPGA boards, our system offers an accessible and scalable tool for the design, real-time simulation, and analysis of large-scale spiking neural networks. PMID:29692702

An FPGA-Based Massively Parallel Neuromorphic Cortex Simulator.

PubMed

Wang, Runchun M; Thakur, Chetan S; van Schaik, André

2018-01-01

This paper presents a massively parallel and scalable neuromorphic cortex simulator designed for simulating large and structurally connected spiking neural networks, such as complex models of various areas of the cortex. The main novelty of this work is the abstraction of a neuromorphic architecture into clusters represented by minicolumns and hypercolumns, analogously to the fundamental structural units observed in neurobiology. Without this approach, simulating large-scale fully connected networks needs prohibitively large memory to store look-up tables for point-to-point connections. Instead, we use a novel architecture, based on the structural connectivity in the neocortex, such that all the required parameters and connections can be stored in on-chip memory. The cortex simulator can be easily reconfigured for simulating different neural networks without any change in hardware structure by programming the memory. A hierarchical communication scheme allows one neuron to have a fan-out of up to 200 k neurons. As a proof-of-concept, an implementation on one Altera Stratix V FPGA was able to simulate 20 million to 2.6 billion leaky-integrate-and-fire (LIF) neurons in real time. We verified the system by emulating a simplified auditory cortex (with 100 million neurons). This cortex simulator achieved a low power dissipation of 1.62 μW per neuron. With the advent of commercially available FPGA boards, our system offers an accessible and scalable tool for the design, real-time simulation, and analysis of large-scale spiking neural networks.

Metal-assisted exfoliation (MAE): green process for transferring graphene to flexible substrates and templating of sub-nanometer plasmonic gaps (Presentation Recording)

NASA Astrophysics Data System (ADS)

Zaretski, Aliaksandr V.; Marin, Brandon C.; Moetazedi, Herad; Dill, Tyler J.; Jibril, Liban; Kong, Casey; Tao, Andrea R.; Lipomi, Darren J.

2015-09-01

This paper describes a new technique, termed "metal-assisted exfoliation," for the scalable transfer of graphene from catalytic copper foils to flexible polymeric supports. The process is amenable to roll-to-roll manufacturing, and the copper substrate can be recycled. We then demonstrate the use of single-layer graphene as a template for the formation of sub-nanometer plasmonic gaps using a scalable fabrication process called "nanoskiving." These gaps are formed between parallel gold nanowires in a process that first produces three-layer thin films with the architecture gold/single-layer graphene/gold, and then sections the composite films with an ultramicrotome. The structures produced can be treated as two gold nanowires separated along their entire lengths by an atomically thin graphene nanoribbon. Oxygen plasma etches the sandwiched graphene to a finite depth; this action produces a sub-nanometer gap near the top surface of the junction between the wires that is capable of supporting highly confined optical fields. The confinement of light is confirmed by surface-enhanced Raman spectroscopy measurements, which indicate that the enhancement of the electric field arises from the junction between the gold nanowires. These experiments demonstrate nanoskiving as a unique and easy-to-implement fabrication technique that is capable of forming sub-nanometer plasmonic gaps between parallel metallic nanostructures over long, macroscopic distances. These structures could be valuable for fundamental investigations as well as applications in plasmonics and molecular electronics.

Factors influencing the implementation, adoption, use, sustainability and scalability of eLearning for family medicine specialty training: a systematic review protocol.

PubMed

Cotič, Živa; Rees, Rebecca; Wark, Petra A; Car, Josip

2016-10-19

In 2013, there was a shortage of approximately 7.2 million health workers worldwide, which is larger among family physicians than among specialists. eLearning could provide a potential solution to some of these global workforce challenges. However, there is little evidence on factors facilitating or hindering implementation, adoption, use, scalability and sustainability of eLearning. This review aims to synthesise results from qualitative and mixed methods studies to provide insight on factors influencing implementation of eLearning for family medicine specialty education and training. Additionally, this review aims to identify the actions needed to increase effectiveness of eLearning and identify the strategies required to improve eLearning implementation, adoption, use, sustainability and scalability for family medicine speciality education and training. A systematic search will be conducted across a range of databases for qualitative studies focusing on experiences, barriers, facilitators, and other factors related to the implementation, adoption, use, sustainability and scalability of eLearning for family medicine specialty education and training. Studies will be synthesised by using the framework analysis approach. This study will contribute to the evaluation of eLearning implementation, adoption, use, sustainability and scalability for family medicine specialty training and education and the development of eLearning guidelines for postgraduate medical education. PROSPERO http://www.crd.york.ac.uk/PROSPERO/display_record.asp?ID=CRD42016036449.

paraGSEA: a scalable approach for large-scale gene expression profiling

PubMed Central

Peng, Shaoliang; Yang, Shunyun

2017-01-01

Abstract More studies have been conducted using gene expression similarity to identify functional connections among genes, diseases and drugs. Gene Set Enrichment Analysis (GSEA) is a powerful analytical method for interpreting gene expression data. However, due to its enormous computational overhead in the estimation of significance level step and multiple hypothesis testing step, the computation scalability and efficiency are poor on large-scale datasets. We proposed paraGSEA for efficient large-scale transcriptome data analysis. By optimization, the overall time complexity of paraGSEA is reduced from O(mn) to O(m+n), where m is the length of the gene sets and n is the length of the gene expression profiles, which contributes more than 100-fold increase in performance compared with other popular GSEA implementations such as GSEA-P, SAM-GS and GSEA2. By further parallelization, a near-linear speed-up is gained on both workstations and clusters in an efficient manner with high scalability and performance on large-scale datasets. The analysis time of whole LINCS phase I dataset (GSE92742) was reduced to nearly half hour on a 1000 node cluster on Tianhe-2, or within 120 hours on a 96-core workstation. The source code of paraGSEA is licensed under the GPLv3 and available at http://github.com/ysycloud/paraGSEA. PMID:28973463

Final Scientific Report: A Scalable Development Environment for Peta-Scale Computing

DOE Office of Scientific and Technical Information (OSTI.GOV)

Karbach, Carsten; Frings, Wolfgang

2013-02-22

This document is the final scientific report of the project DE-SC000120 (A scalable Development Environment for Peta-Scale Computing). The objective of this project is the extension of the Parallel Tools Platform (PTP) for applying it to peta-scale systems. PTP is an integrated development environment for parallel applications. It comprises code analysis, performance tuning, parallel debugging and system monitoring. The contribution of the Juelich Supercomputing Centre (JSC) aims to provide a scalable solution for system monitoring of supercomputers. This includes the development of a new communication protocol for exchanging status data between the target remote system and the client running PTP.more » The communication has to work for high latency. PTP needs to be implemented robustly and should hide the complexity of the supercomputer's architecture in order to provide a transparent access to various remote systems via a uniform user interface. This simplifies the porting of applications to different systems, because PTP functions as abstraction layer between parallel application developer and compute resources. The common requirement for all PTP components is that they have to interact with the remote supercomputer. E.g. applications are built remotely and performance tools are attached to job submissions and their output data resides on the remote system. Status data has to be collected by evaluating outputs of the remote job scheduler and the parallel debugger needs to control an application executed on the supercomputer. The challenge is to provide this functionality for peta-scale systems in real-time. The client server architecture of the established monitoring application LLview, developed by the JSC, can be applied to PTP's system monitoring. LLview provides a well-arranged overview of the supercomputer's current status. A set of statistics, a list of running and queued jobs as well as a node display mapping running jobs to their compute resources form the user display of LLview. These monitoring features have to be integrated into the development environment. Besides showing the current status PTP's monitoring also needs to allow for submitting and canceling user jobs. Monitoring peta-scale systems especially deals with presenting the large amount of status data in a useful manner. Users require to select arbitrary levels of detail. The monitoring views have to provide a quick overview of the system state, but also need to allow for zooming into specific parts of the system, into which the user is interested in. At present, the major batch systems running on supercomputers are PBS, TORQUE, ALPS and LoadLeveler, which have to be supported by both the monitoring and the job controlling component. Finally, PTP needs to be designed as generic as possible, so that it can be extended for future batch systems.« less

Snow Micro-Structure Model

DOE Office of Scientific and Technical Information (OSTI.GOV)

Micah Johnson, Andrew Slaughter

PIKA is a MOOSE-based application for modeling micro-structure evolution of seasonal snow. The model will be useful for environmental, atmospheric, and climate scientists. Possible applications include application to energy balance models, ice sheet modeling, and avalanche forecasting. The model implements physics from published, peer-reviewed articles. The main purpose is to foster university and laboratory collaboration to build a larger multi-scale snow model using MOOSE. The main feature of the code is that it is implemented using the MOOSE framework, thus making features such as multiphysics coupling, adaptive mesh refinement, and parallel scalability native to the application. PIKA implements three equations:more » the phase-field equation for tracking the evolution of the ice-air interface within seasonal snow at the grain-scale; the heat equation for computing the temperature of both the ice and air within the snow; and the mass transport equation for monitoring the diffusion of water vapor in the pore space of the snow.« less

IGA-ADS: Isogeometric analysis FEM using ADS solver

NASA Astrophysics Data System (ADS)

Łoś, Marcin M.; Woźniak, Maciej; Paszyński, Maciej; Lenharth, Andrew; Hassaan, Muhamm Amber; Pingali, Keshav

2017-08-01

In this paper we present a fast explicit solver for solution of non-stationary problems using L2 projections with isogeometric finite element method. The solver has been implemented within GALOIS framework. It enables parallel multi-core simulations of different time-dependent problems, in 1D, 2D, or 3D. We have prepared the solver framework in a way that enables direct implementation of the selected PDE and corresponding boundary conditions. In this paper we describe the installation, implementation of exemplary three PDEs, and execution of the simulations on multi-core Linux cluster nodes. We consider three case studies, including heat transfer, linear elasticity, as well as non-linear flow in heterogeneous media. The presented package generates output suitable for interfacing with Gnuplot and ParaView visualization software. The exemplary simulations show near perfect scalability on Gilbert shared-memory node with four Intel® Xeon® CPU E7-4860 processors, each possessing 10 physical cores (for a total of 40 cores).

The connection-set algebra--a novel formalism for the representation of connectivity structure in neuronal network models.

PubMed

Djurfeldt, Mikael

2012-07-01

The connection-set algebra (CSA) is a novel and general formalism for the description of connectivity in neuronal network models, from small-scale to large-scale structure. The algebra provides operators to form more complex sets of connections from simpler ones and also provides parameterization of such sets. CSA is expressive enough to describe a wide range of connection patterns, including multiple types of random and/or geometrically dependent connectivity, and can serve as a concise notation for network structure in scientific writing. CSA implementations allow for scalable and efficient representation of connectivity in parallel neuronal network simulators and could even allow for avoiding explicit representation of connections in computer memory. The expressiveness of CSA makes prototyping of network structure easy. A C+ + version of the algebra has been implemented and used in a large-scale neuronal network simulation (Djurfeldt et al., IBM J Res Dev 52(1/2):31-42, 2008b) and an implementation in Python has been publicly released.

Perspectives on the Future of CFD

NASA Technical Reports Server (NTRS)

Kwak, Dochan

2000-01-01

This viewgraph presentation gives an overview of the future of computational fluid dynamics (CFD), which in the past has pioneered the field of flow simulation. Over time CFD has progressed as computing power. Numerical methods have been advanced as CPU and memory capacity increases. Complex configurations are routinely computed now and direct numerical simulations (DNS) and large eddy simulations (LES) are used to study turbulence. As the computing resources changed to parallel and distributed platforms, computer science aspects such as scalability (algorithmic and implementation) and portability and transparent codings have advanced. Examples of potential future (or current) challenges include risk assessment, limitations of the heuristic model, and the development of CFD and information technology (IT) tools.

A Low-Power Instruction Issue Queue for Microprocessors

NASA Astrophysics Data System (ADS)

Watanabe, Shingo; Chiyonobu, Akihiro; Sato, Toshinori

Instruction issue queue is a key component which extracts instruction level parallelism (ILP) in modern out-of-order microprocessors. In order to exploit ILP for improving processor performance, instruction queue size should be increased. However, it is difficult to increase the size, since instruction queue is implemented by a content addressable memory (CAM) whose power and delay are much large. This paper introduces a low power and scalable instruction queue that replaces the CAM with a RAM. In this queue, instructions are explicitly woken up. Evaluation results show that the proposed instruction queue decreases processor performance by only 1.9% on average. Furthermore, the total energy consumption is reduced by 54% on average.

SeqPig: simple and scalable scripting for large sequencing data sets in Hadoop.

PubMed

Schumacher, André; Pireddu, Luca; Niemenmaa, Matti; Kallio, Aleksi; Korpelainen, Eija; Zanetti, Gianluigi; Heljanko, Keijo

2014-01-01

Hadoop MapReduce-based approaches have become increasingly popular due to their scalability in processing large sequencing datasets. However, as these methods typically require in-depth expertise in Hadoop and Java, they are still out of reach of many bioinformaticians. To solve this problem, we have created SeqPig, a library and a collection of tools to manipulate, analyze and query sequencing datasets in a scalable and simple manner. SeqPigscripts use the Hadoop-based distributed scripting engine Apache Pig, which automatically parallelizes and distributes data processing tasks. We demonstrate SeqPig's scalability over many computing nodes and illustrate its use with example scripts. Available under the open source MIT license at http://sourceforge.net/projects/seqpig/

Large-scale parallel genome assembler over cloud computing environment.

PubMed

Das, Arghya Kusum; Koppa, Praveen Kumar; Goswami, Sayan; Platania, Richard; Park, Seung-Jong

2017-06-01

The size of high throughput DNA sequencing data has already reached the terabyte scale. To manage this huge volume of data, many downstream sequencing applications started using locality-based computing over different cloud infrastructures to take advantage of elastic (pay as you go) resources at a lower cost. However, the locality-based programming model (e.g. MapReduce) is relatively new. Consequently, developing scalable data-intensive bioinformatics applications using this model and understanding the hardware environment that these applications require for good performance, both require further research. In this paper, we present a de Bruijn graph oriented Parallel Giraph-based Genome Assembler (GiGA), as well as the hardware platform required for its optimal performance. GiGA uses the power of Hadoop (MapReduce) and Giraph (large-scale graph analysis) to achieve high scalability over hundreds of compute nodes by collocating the computation and data. GiGA achieves significantly higher scalability with competitive assembly quality compared to contemporary parallel assemblers (e.g. ABySS and Contrail) over traditional HPC cluster. Moreover, we show that the performance of GiGA is significantly improved by using an SSD-based private cloud infrastructure over traditional HPC cluster. We observe that the performance of GiGA on 256 cores of this SSD-based cloud infrastructure closely matches that of 512 cores of traditional HPC cluster.

Application of a Scalable, Parallel, Unstructured-Grid-Based Navier-Stokes Solver

NASA Technical Reports Server (NTRS)

Parikh, Paresh

2001-01-01

A parallel version of an unstructured-grid based Navier-Stokes solver, USM3Dns, previously developed for efficient operation on a variety of parallel computers, has been enhanced to incorporate upgrades made to the serial version. The resultant parallel code has been extensively tested on a variety of problems of aerospace interest and on two sets of parallel computers to understand and document its characteristics. An innovative grid renumbering construct and use of non-blocking communication are shown to produce superlinear computing performance. Preliminary results from parallelization of a recently introduced "porous surface" boundary condition are also presented.

Parallel computation with molecular-motor-propelled agents in nanofabricated networks.

PubMed

Nicolau, Dan V; Lard, Mercy; Korten, Till; van Delft, Falco C M J M; Persson, Malin; Bengtsson, Elina; Månsson, Alf; Diez, Stefan; Linke, Heiner; Nicolau, Dan V

2016-03-08

The combinatorial nature of many important mathematical problems, including nondeterministic-polynomial-time (NP)-complete problems, places a severe limitation on the problem size that can be solved with conventional, sequentially operating electronic computers. There have been significant efforts in conceiving parallel-computation approaches in the past, for example: DNA computation, quantum computation, and microfluidics-based computation. However, these approaches have not proven, so far, to be scalable and practical from a fabrication and operational perspective. Here, we report the foundations of an alternative parallel-computation system in which a given combinatorial problem is encoded into a graphical, modular network that is embedded in a nanofabricated planar device. Exploring the network in a parallel fashion using a large number of independent, molecular-motor-propelled agents then solves the mathematical problem. This approach uses orders of magnitude less energy than conventional computers, thus addressing issues related to power consumption and heat dissipation. We provide a proof-of-concept demonstration of such a device by solving, in a parallel fashion, the small instance {2, 5, 9} of the subset sum problem, which is a benchmark NP-complete problem. Finally, we discuss the technical advances necessary to make our system scalable with presently available technology.

Geocomputation over Hybrid Computer Architecture and Systems: Prior Works and On-going Initiatives at UARK

NASA Astrophysics Data System (ADS)

Shi, X.

2015-12-01

As NSF indicated - "Theory and experimentation have for centuries been regarded as two fundamental pillars of science. It is now widely recognized that computational and data-enabled science forms a critical third pillar." Geocomputation is the third pillar of GIScience and geosciences. With the exponential growth of geodata, the challenge of scalable and high performance computing for big data analytics become urgent because many research activities are constrained by the inability of software or tool that even could not complete the computation process. Heterogeneous geodata integration and analytics obviously magnify the complexity and operational time frame. Many large-scale geospatial problems may be not processable at all if the computer system does not have sufficient memory or computational power. Emerging computer architectures, such as Intel's Many Integrated Core (MIC) Architecture and Graphics Processing Unit (GPU), and advanced computing technologies provide promising solutions to employ massive parallelism and hardware resources to achieve scalability and high performance for data intensive computing over large spatiotemporal and social media data. Exploring novel algorithms and deploying the solutions in massively parallel computing environment to achieve the capability for scalable data processing and analytics over large-scale, complex, and heterogeneous geodata with consistent quality and high-performance has been the central theme of our research team in the Department of Geosciences at the University of Arkansas (UARK). New multi-core architectures combined with application accelerators hold the promise to achieve scalability and high performance by exploiting task and data levels of parallelism that are not supported by the conventional computing systems. Such a parallel or distributed computing environment is particularly suitable for large-scale geocomputation over big data as proved by our prior works, while the potential of such advanced infrastructure remains unexplored in this domain. Within this presentation, our prior and on-going initiatives will be summarized to exemplify how we exploit multicore CPUs, GPUs, and MICs, and clusters of CPUs, GPUs and MICs, to accelerate geocomputation in different applications.

A Parallel Ghosting Algorithm for The Flexible Distributed Mesh Database

DOE PAGES

Mubarak, Misbah; Seol, Seegyoung; Lu, Qiukai; ...

2013-01-01

Critical to the scalability of parallel adaptive simulations are parallel control functions including load balancing, reduced inter-process communication and optimal data decomposition. In distributed meshes, many mesh-based applications frequently access neighborhood information for computational purposes which must be transmitted efficiently to avoid parallel performance degradation when the neighbors are on different processors. This article presents a parallel algorithm of creating and deleting data copies, referred to as ghost copies, which localize neighborhood data for computation purposes while minimizing inter-process communication. The key characteristics of the algorithm are: (1) It can create ghost copies of any permissible topological order in amore » 1D, 2D or 3D mesh based on selected adjacencies. (2) It exploits neighborhood communication patterns during the ghost creation process thus eliminating all-to-all communication. (3) For applications that need neighbors of neighbors, the algorithm can create n number of ghost layers up to a point where the whole partitioned mesh can be ghosted. Strong and weak scaling results are presented for the IBM BG/P and Cray XE6 architectures up to a core count of 32,768 processors. The algorithm also leads to scalable results when used in a parallel super-convergent patch recovery error estimator, an application that frequently accesses neighborhood data to carry out computation.« less

A Hybrid Parallel Strategy Based on String Graph Theory to Improve De Novo DNA Assembly on the TianHe-2 Supercomputer.

PubMed

Zhang, Feng; Liao, Xiangke; Peng, Shaoliang; Cui, Yingbo; Wang, Bingqiang; Zhu, Xiaoqian; Liu, Jie

2016-06-01

' The de novo assembly of DNA sequences is increasingly important for biological researches in the genomic era. After more than one decade since the Human Genome Project, some challenges still exist and new solutions are being explored to improve de novo assembly of genomes. String graph assembler (SGA), based on the string graph theory, is a new method/tool developed to address the challenges. In this paper, based on an in-depth analysis of SGA we prove that the SGA-based sequence de novo assembly is an NP-complete problem. According to our analysis, SGA outperforms other similar methods/tools in memory consumption, but costs much more time, of which 60-70 % is spent on the index construction. Upon this analysis, we introduce a hybrid parallel optimization algorithm and implement this algorithm in the TianHe-2's parallel framework. Simulations are performed with different datasets. For data of small size the optimized solution is 3.06 times faster than before, and for data of middle size it's 1.60 times. The results demonstrate an evident performance improvement, with the linear scalability for parallel FM-index construction. This results thus contribute significantly to improving the efficiency of de novo assembly of DNA sequences.

Embedded ensemble propagation for improving performance, portability, and scalability of uncertainty quantification on emerging computational architectures

DOE PAGES

Phipps, Eric T.; D'Elia, Marta; Edwards, Harold C.; ...

2017-04-18

In this study, quantifying simulation uncertainties is a critical component of rigorous predictive simulation. A key component of this is forward propagation of uncertainties in simulation input data to output quantities of interest. Typical approaches involve repeated sampling of the simulation over the uncertain input data, and can require numerous samples when accurately propagating uncertainties from large numbers of sources. Often simulation processes from sample to sample are similar and much of the data generated from each sample evaluation could be reused. We explore a new method for implementing sampling methods that simultaneously propagates groups of samples together in anmore » embedded fashion, which we call embedded ensemble propagation. We show how this approach takes advantage of properties of modern computer architectures to improve performance by enabling reuse between samples, reducing memory bandwidth requirements, improving memory access patterns, improving opportunities for fine-grained parallelization, and reducing communication costs. We describe a software technique for implementing embedded ensemble propagation based on the use of C++ templates and describe its integration with various scientific computing libraries within Trilinos. We demonstrate improved performance, portability and scalability for the approach applied to the simulation of partial differential equations on a variety of CPU, GPU, and accelerator architectures, including up to 131,072 cores on a Cray XK7 (Titan).« less

The PMS project: Poor man's supercomputer

NASA Astrophysics Data System (ADS)

Csikor, F.; Fodor, Z.; Hegedüs, P.; Horváth, V. K.; Katz, S. D.; Piróth, A.

2001-02-01

We briefly describe the Poor Man's Supercomputer (PMS) project carried out at Eötvös University, Budapest. The goal was to construct a cost effective, scalable, fast parallel computer to perform numerical calculations of physical problems that can be implemented on a lattice with nearest neighbour interactions. To this end we developed the PMS architecture using PC components and designed a special, low cost communication hardware and the driver software for Linux OS. Our first implementation of PMS includes 32 nodes (PMS1). The performance of PMS1 was tested by Lattice Gauge Theory simulations. Using pure SU(3) gauge theory or the bosonic part of the minimal supersymmetric extention of the standard model (MSSM) on PMS1 we obtained 3 / Mflops and 0.60 / Mflops price-to-sustained performance ratio for double and single precision operations, respectively. The design of the special hardware and the communication driver are freely available upon request for non-profit organizations.

Near-realtime simulations of biolelectric activity in small mammalian hearts using graphical processing units

PubMed Central

Vigmond, Edward J.; Boyle, Patrick M.; Leon, L. Joshua; Plank, Gernot

2014-01-01

Simulations of cardiac bioelectric phenomena remain a significant challenge despite continual advancements in computational machinery. Spanning large temporal and spatial ranges demands millions of nodes to accurately depict geometry, and a comparable number of timesteps to capture dynamics. This study explores a new hardware computing paradigm, the graphics processing unit (GPU), to accelerate cardiac models, and analyzes results in the context of simulating a small mammalian heart in real time. The ODEs associated with membrane ionic flow were computed on traditional CPU and compared to GPU performance, for one to four parallel processing units. The scalability of solving the PDE responsible for tissue coupling was examined on a cluster using up to 128 cores. Results indicate that the GPU implementation was between 9 and 17 times faster than the CPU implementation and scaled similarly. Solving the PDE was still 160 times slower than real time. PMID:19964295

Quantum simulator review

NASA Astrophysics Data System (ADS)

Bednar, Earl; Drager, Steven L.

2007-04-01

Quantum information processing's objective is to utilize revolutionary computing capability based on harnessing the paradigm shift offered by quantum computing to solve classically hard and computationally challenging problems. Some of our computationally challenging problems of interest include: the capability for rapid image processing, rapid optimization of logistics, protecting information, secure distributed simulation, and massively parallel computation. Currently, one important problem with quantum information processing is that the implementation of quantum computers is difficult to realize due to poor scalability and great presence of errors. Therefore, we have supported the development of Quantum eXpress and QuIDD Pro, two quantum computer simulators running on classical computers for the development and testing of new quantum algorithms and processes. This paper examines the different methods used by these two quantum computing simulators. It reviews both simulators, highlighting each simulators background, interface, and special features. It also demonstrates the implementation of current quantum algorithms on each simulator. It concludes with summary comments on both simulators.

Smart-Pixel Array Processors Based on Optimal Cellular Neural Networks for Space Sensor Applications

NASA Technical Reports Server (NTRS)

Fang, Wai-Chi; Sheu, Bing J.; Venus, Holger; Sandau, Rainer

1997-01-01

A smart-pixel cellular neural network (CNN) with hardware annealing capability, digitally programmable synaptic weights, and multisensor parallel interface has been under development for advanced space sensor applications. The smart-pixel CNN architecture is a programmable multi-dimensional array of optoelectronic neurons which are locally connected with their local neurons and associated active-pixel sensors. Integration of the neuroprocessor in each processor node of a scalable multiprocessor system offers orders-of-magnitude computing performance enhancements for on-board real-time intelligent multisensor processing and control tasks of advanced small satellites. The smart-pixel CNN operation theory, architecture, design and implementation, and system applications are investigated in detail. The VLSI (Very Large Scale Integration) implementation feasibility was illustrated by a prototype smart-pixel 5x5 neuroprocessor array chip of active dimensions 1380 micron x 746 micron in a 2-micron CMOS technology.

Improvements on non-equilibrium and transport Green function techniques: The next-generation TRANSIESTA

NASA Astrophysics Data System (ADS)

Papior, Nick; Lorente, Nicolás; Frederiksen, Thomas; García, Alberto; Brandbyge, Mads

2017-03-01

We present novel methods implemented within the non-equilibrium Green function code (NEGF) TRANSIESTA based on density functional theory (DFT). Our flexible, next-generation DFT-NEGF code handles devices with one or multiple electrodes (Ne ≥ 1) with individual chemical potentials and electronic temperatures. We describe its novel methods for electrostatic gating, contour optimizations, and assertion of charge conservation, as well as the newly implemented algorithms for optimized and scalable matrix inversion, performance-critical pivoting, and hybrid parallelization. Additionally, a generic NEGF "post-processing" code (TBTRANS/PHTRANS) for electron and phonon transport is presented with several novelties such as Hamiltonian interpolations, Ne ≥ 1 electrode capability, bond-currents, generalized interface for user-defined tight-binding transport, transmission projection using eigenstates of a projected Hamiltonian, and fast inversion algorithms for large-scale simulations easily exceeding 106 atoms on workstation computers. The new features of both codes are demonstrated and bench-marked for relevant test systems.

A genetic algorithm-based job scheduling model for big data analytics.

PubMed

Lu, Qinghua; Li, Shanshan; Zhang, Weishan; Zhang, Lei

Big data analytics (BDA) applications are a new category of software applications that process large amounts of data using scalable parallel processing infrastructure to obtain hidden value. Hadoop is the most mature open-source big data analytics framework, which implements the MapReduce programming model to process big data with MapReduce jobs. Big data analytics jobs are often continuous and not mutually separated. The existing work mainly focuses on executing jobs in sequence, which are often inefficient and consume high energy. In this paper, we propose a genetic algorithm-based job scheduling model for big data analytics applications to improve the efficiency of big data analytics. To implement the job scheduling model, we leverage an estimation module to predict the performance of clusters when executing analytics jobs. We have evaluated the proposed job scheduling model in terms of feasibility and accuracy.

Towards Highly Scalable Ab Initio Molecular Dynamics (AIMD) Simulations on the Intel Knights Landing Manycore Processor

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jacquelin, Mathias; De Jong, Wibe A.; Bylaska, Eric J.

2017-07-03

The Ab Initio Molecular Dynamics (AIMD) method allows scientists to treat the dynamics of molecular and condensed phase systems while retaining a first-principles-based description of their interactions. This extremely important method has tremendous computational requirements, because the electronic Schr¨odinger equation, approximated using Kohn-Sham Density Functional Theory (DFT), is solved at every time step. With the advent of manycore architectures, application developers have a significant amount of processing power within each compute node that can only be exploited through massive parallelism. A compute intensive application such as AIMD forms a good candidate to leverage this processing power. In this paper, wemore » focus on adding thread level parallelism to the plane wave DFT methodology implemented in NWChem. Through a careful optimization of tall-skinny matrix products, which are at the heart of the Lagrange multiplier and nonlocal pseudopotential kernels, as well as 3D FFTs, our OpenMP implementation delivers excellent strong scaling on the latest Intel Knights Landing (KNL) processor. We assess the efficiency of our Lagrange multiplier kernels by building a Roofline model of the platform, and verify that our implementation is close to the roofline for various problem sizes. Finally, we present strong scaling results on the complete AIMD simulation for a 64 water molecules test case, that scales up to all 68 cores of the Knights Landing processor.« less

PIXIE3D: A Parallel, Implicit, eXtended MHD 3D Code

NASA Astrophysics Data System (ADS)

Chacon, Luis

2006-10-01

We report on the development of PIXIE3D, a 3D parallel, fully implicit Newton-Krylov extended MHD code in general curvilinear geometry. PIXIE3D employs a second-order, finite-volume-based spatial discretization that satisfies remarkable properties such as being conservative, solenoidal in the magnetic field to machine precision, non-dissipative, and linearly and nonlinearly stable in the absence of physical dissipation. PIXIE3D employs fully-implicit Newton-Krylov methods for the time advance. Currently, second-order implicit schemes such as Crank-Nicolson and BDF2 (2^nd order backward differentiation formula) are available. PIXIE3D is fully parallel (employs PETSc for parallelism), and exhibits excellent parallel scalability. A parallel, scalable, MG preconditioning strategy, based on physics-based preconditioning ideas, has been developed for resistive MHD, and is currently being extended to Hall MHD. In this poster, we will report on progress in the algorithmic formulation for extended MHD, as well as the the serial and parallel performance of PIXIE3D in a variety of problems and geometries. L. Chac'on, Comput. Phys. Comm., 163 (3), 143-171 (2004) L. Chac'on et al., J. Comput. Phys. 178 (1), 15- 36 (2002); J. Comput. Phys., 188 (2), 573-592 (2003) L. Chac'on, 32nd EPS Conf. Plasma Physics, Tarragona, Spain, 2005 L. Chac'on et al., 33rd EPS Conf. Plasma Physics, Rome, Italy, 2006

Scalable Parallel Algorithms for Multidimensional Digital Signal Processing

DTIC Science & Technology

1991-12-31

Proceedings, San Diego CL., August 1989, pp. 132-146. 53 [13] A. L. Gorin, L. Auslander, and A. Silberger . Balanced computation of 2D trans- forms on a tree...Speech, Signal Processing. ASSP-34, Oct. 1986,pp. 1301-1309. [24] A. Norton and A. Silberger . Parallelization and performance analysis of the Cooley-Tukey

Parallel O(N) Stokes’ solver towards scalable Brownian dynamics of hydrodynamically interacting objects in general geometries

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhao, Xujun; Li, Jiyuan; Jiang, Xikai

An efficient parallel Stokes’s solver is developed towards the complete inclusion of hydrodynamic interactions of Brownian particles in any geometry. A Langevin description of the particle dynamics is adopted, where the long-range interactions are included using a Green’s function formalism. We present a scalable parallel computational approach, where the general geometry Stokeslet is calculated following a matrix-free algorithm using the General geometry Ewald-like method. Our approach employs a highly-efficient iterative finite element Stokes’ solver for the accurate treatment of long-range hydrodynamic interactions within arbitrary confined geometries. A combination of mid-point time integration of the Brownian stochastic differential equation, the parallelmore » Stokes’ solver, and a Chebyshev polynomial approximation for the fluctuation-dissipation theorem result in an O(N) parallel algorithm. We also illustrate the new algorithm in the context of the dynamics of confined polymer solutions in equilibrium and non-equilibrium conditions. Our method is extended to treat suspended finite size particles of arbitrary shape in any geometry using an Immersed Boundary approach.« less

Parallel O(N) Stokes’ solver towards scalable Brownian dynamics of hydrodynamically interacting objects in general geometries

DOE PAGES

Zhao, Xujun; Li, Jiyuan; Jiang, Xikai; ...

2017-06-29

An efficient parallel Stokes’s solver is developed towards the complete inclusion of hydrodynamic interactions of Brownian particles in any geometry. A Langevin description of the particle dynamics is adopted, where the long-range interactions are included using a Green’s function formalism. We present a scalable parallel computational approach, where the general geometry Stokeslet is calculated following a matrix-free algorithm using the General geometry Ewald-like method. Our approach employs a highly-efficient iterative finite element Stokes’ solver for the accurate treatment of long-range hydrodynamic interactions within arbitrary confined geometries. A combination of mid-point time integration of the Brownian stochastic differential equation, the parallelmore » Stokes’ solver, and a Chebyshev polynomial approximation for the fluctuation-dissipation theorem result in an O(N) parallel algorithm. We also illustrate the new algorithm in the context of the dynamics of confined polymer solutions in equilibrium and non-equilibrium conditions. Our method is extended to treat suspended finite size particles of arbitrary shape in any geometry using an Immersed Boundary approach.« less

Benchmark coupled-cluster g-tensor calculations with full inclusion of the two-particle spin-orbit contributions.

PubMed

Perera, Ajith; Gauss, Jürgen; Verma, Prakash; Morales, Jorge A

2017-04-28

We present a parallel implementation to compute electron spin resonance g-tensors at the coupled-cluster singles and doubles (CCSD) level which employs the ACES III domain-specific software tools for scalable parallel programming, i.e., the super instruction architecture language and processor (SIAL and SIP), respectively. A unique feature of the present implementation is the exact (not approximated) inclusion of the five one- and two-particle contributions to the g-tensor [i.e., the mass correction, one- and two-particle paramagnetic spin-orbit, and one- and two-particle diamagnetic spin-orbit terms]. Like in a previous implementation with effective one-electron operators [J. Gauss et al., J. Phys. Chem. A 113, 11541-11549 (2009)], our implementation utilizes analytic CC second derivatives and, therefore, classifies as a true CC linear-response treatment. Therefore, our implementation can unambiguously appraise the accuracy of less costly effective one-particle schemes and provide a rationale for their widespread use. We have considered a large selection of radicals used previously for benchmarking purposes including those studied in earlier work and conclude that at the CCSD level, the effective one-particle scheme satisfactorily captures the two-particle effects less costly than the rigorous two-particle scheme. With respect to the performance of density functional theory (DFT), we note that results obtained with the B3LYP functional exhibit the best agreement with our CCSD results. However, in general, the CCSD results agree better with the experimental data than the best DFT/B3LYP results, although in most cases within the rather large experimental error bars.

Parallel Unsteady Overset Mesh Methodology for Adaptive and Moving Grids with Multiple Solvers

DTIC Science & Technology

2010-01-01

Research Laboratory Hampton, Virginia Jayanarayanan Sitaraman National Institute of Aerospace Hampton, Virginia ABSTRACT This paper describes a new...Army Research Laboratory ,Hampton, VA, , , 8. PERFORMING ORGANIZATION REPORT NUMBER 9. SPONSORING/MONITORING AGENCY NAME(S) AND ADDRESS(ES) NATO/RTO...results section ( 3.6 and 3.5). Good linear scalability was observed for all three cases up to 12 processors. Beyond that the scalability drops off

Properties of the numerical algorithms for problems of quantum information technologies: Benefits of deep analysis

NASA Astrophysics Data System (ADS)

Chernyavskiy, Andrey; Khamitov, Kamil; Teplov, Alexey; Voevodin, Vadim; Voevodin, Vladimir

2016-10-01

In recent years, quantum information technologies (QIT) showed great development, although, the way of the implementation of QIT faces the serious difficulties, some of which are challenging computational tasks. This work is devoted to the deep and broad analysis of the parallel algorithmic properties of such tasks. As an example we take one- and two-qubit transformations of a many-qubit quantum state, which are the most critical kernels of many important QIT applications. The analysis of the algorithms uses the methodology of the AlgoWiki project (algowiki-project.org) and consists of two parts: theoretical and experimental. Theoretical part includes features like sequential and parallel complexity, macro structure, and visual information graph. Experimental part was made by using the petascale Lomonosov supercomputer (Moscow State University, Russia) and includes the analysis of locality and memory access, scalability and the set of more specific dynamic characteristics of realization. This approach allowed us to obtain bottlenecks and generate ideas of efficiency improvement.

Performance assessment of KORAT-3D on the ANL IBM-SP computer

DOE Office of Scientific and Technical Information (OSTI.GOV)

Alexeyev, A.V.; Zvenigorodskaya, O.A.; Shagaliev, R.M.

1999-09-01

The TENAR code is currently being developed at the Russian Federal Nuclear Center (VNIIEF) as a coupled dynamics code for the simulation of transients in VVER and RBMK systems and other nuclear systems. The neutronic module in this code system is KORAT-3D. This module is also one of the most computationally intensive components of the code system. A parallel version of KORAT-3D has been implemented to achieve the goal of obtaining transient solutions in reasonable computational time, particularly for RBMK calculations that involve the application of >100,000 nodes. An evaluation of the KORAT-3D code performance was recently undertaken on themore » Argonne National Laboratory (ANL) IBM ScalablePower (SP) parallel computer located in the Mathematics and Computer Science Division of ANL. At the time of the study, the ANL IBM-SP computer had 80 processors. This study was conducted under the auspices of a technical staff exchange program sponsored by the International Nuclear Safety Center (INSC).« less

GPU accelerated particle visualization with Splotch

NASA Astrophysics Data System (ADS)

Rivi, M.; Gheller, C.; Dykes, T.; Krokos, M.; Dolag, K.

2014-07-01

Splotch is a rendering algorithm for exploration and visual discovery in particle-based datasets coming from astronomical observations or numerical simulations. The strengths of the approach are production of high quality imagery and support for very large-scale datasets through an effective mix of the OpenMP and MPI parallel programming paradigms. This article reports our experiences in re-designing Splotch for exploiting emerging HPC architectures nowadays increasingly populated with GPUs. A performance model is introduced to guide our re-factoring of Splotch. A number of parallelization issues are discussed, in particular relating to race conditions and workload balancing, towards achieving optimal performances. Our implementation was accomplished by using the CUDA programming paradigm. Our strategy is founded on novel schemes achieving optimized data organization and classification of particles. We deploy a reference cosmological simulation to present performance results on acceleration gains and scalability. We finally outline our vision for future work developments including possibilities for further optimizations and exploitation of hybrid systems and emerging accelerators.

Studying an Eulerian Computer Model on Different High-performance Computer Platforms and Some Applications

NASA Astrophysics Data System (ADS)

Georgiev, K.; Zlatev, Z.

2010-11-01

The Danish Eulerian Model (DEM) is an Eulerian model for studying the transport of air pollutants on large scale. Originally, the model was developed at the National Environmental Research Institute of Denmark. The model computational domain covers Europe and some neighbour parts belong to the Atlantic Ocean, Asia and Africa. If DEM model is to be applied by using fine grids, then its discretization leads to a huge computational problem. This implies that such a model as DEM must be run only on high-performance computer architectures. The implementation and tuning of such a complex large-scale model on each different computer is a non-trivial task. Here, some comparison results of running of this model on different kind of vector (CRAY C92A, Fujitsu, etc.), parallel computers with distributed memory (IBM SP, CRAY T3E, Beowulf clusters, Macintosh G4 clusters, etc.), parallel computers with shared memory (SGI Origin, SUN, etc.) and parallel computers with two levels of parallelism (IBM SMP, IBM BlueGene/P, clusters of multiprocessor nodes, etc.) will be presented. The main idea in the parallel version of DEM is domain partitioning approach. Discussions according to the effective use of the cache and hierarchical memories of the modern computers as well as the performance, speed-ups and efficiency achieved will be done. The parallel code of DEM, created by using MPI standard library, appears to be highly portable and shows good efficiency and scalability on different kind of vector and parallel computers. Some important applications of the computer model output are presented in short.

Serial Back-Plane Technologies in Advanced Avionics Architectures

NASA Technical Reports Server (NTRS)

Varnavas, Kosta

2005-01-01

Current back plane technologies such as VME, and current personal computer back planes such as PCI, are shared bus systems that can exhibit nondeterministic latencies. This means a card can take control of the bus and use resources indefinitely affecting the ability of other cards in the back plane to acquire the bus. This provides a real hit on the reliability of the system. Additionally, these parallel busses only have bandwidths in the 100s of megahertz range and EMI and noise effects get worse the higher the bandwidth goes. To provide scalable, fault-tolerant, advanced computing systems, more applicable to today s connected computing environment and to better meet the needs of future requirements for advanced space instruments and vehicles, serial back-plane technologies should be implemented in advanced avionics architectures. Serial backplane technologies eliminate the problem of one card getting the bus and never relinquishing it, or one minor problem on the backplane bringing the whole system down. Being serial instead of parallel improves the reliability by reducing many of the signal integrity issues associated with parallel back planes and thus significantly improves reliability. The increased speeds associated with a serial backplane are an added bonus.

Effectiveness and micro-costing of the KiVa school-based bullying prevention programme in Wales: study protocol for a pragmatic definitive parallel group cluster randomised controlled trial.

PubMed

Clarkson, Suzy; Axford, Nick; Berry, Vashti; Edwards, Rhiannon Tudor; Bjornstad, Gretchen; Wrigley, Zoe; Charles, Joanna; Hoare, Zoe; Ukoumunne, Obioha C; Matthews, Justin; Hutchings, Judy

2016-02-01

Bullying refers to verbal, physical or psychological aggression repeated over time that is intended to cause harm or distress to the victims who are unable to defend themselves. It is a key public health priority owing to its widespread prevalence in schools and harmful short- and long-term effects on victims' well-being. There is a need to strengthen the evidence base by testing innovative approaches to preventing bullying. KiVa is a school-based bullying prevention programme with universal and indicated elements and an emphasis on changing bystander behaviour. It achieved promising results in a large trial in Finland, and now requires testing in other countries. This paper describes the protocol for a cluster randomised controlled trial (RCT) of KiVa in Wales. The study uses a two-arm waitlist control pragmatic definitive parallel group cluster RCT design with an embedded process evaluation and calculation of unit cost. Participating schools will be randomised a using a 1:1 ratio to KiVa plus usual provision (intervention group) or usual provision only (control group). The trial has one primary outcome, child self-reported victimisation from bullying, dichotomised as 'victimised' (bullied at least twice a month in the last couple of months) versus 'not victimised'. Secondary outcomes are: bullying perpetration; aspects of child social and emotional well-being (including emotional problems, conduct, peer relations, prosocial behaviour); and school attendance. Follow-up is at 12 months post-baseline. Implementation fidelity is measured through teacher-completed lesson records and independent school-wide observation. A micro-costing analysis will determine the costs of implementing KiVa, including recurrent and non-recurrent unit costs. Factors related to the scalability of the programme will be examined in interviews with head teachers and focus groups with key stakeholders in the implementation of school-based bullying interventions. The results from this trial will provide evidence on whether the KiVa programme is transportable from Finland to Wales in terms of effectiveness and implementation. It will provide information about the costs of delivery and generate insights into factors related to the scalability of the programme. Current Controlled Trials ISRCTN23999021 Date 10-6-13.

Improving the Efficiency of Free Energy Calculations in the Amber Molecular Dynamics Package.

PubMed

Kaus, Joseph W; Pierce, Levi T; Walker, Ross C; McCammont, J Andrew

2013-09-10

Alchemical transformations are widely used methods to calculate free energies. Amber has traditionally included support for alchemical transformations as part of the sander molecular dynamics (MD) engine. Here we describe the implementation of a more efficient approach to alchemical transformations in the Amber MD package. Specifically we have implemented this new approach within the more computational efficient and scalable pmemd MD engine that is included with the Amber MD package. The majority of the gain in efficiency comes from the improved design of the calculation, which includes better parallel scaling and reduction in the calculation of redundant terms. This new implementation is able to reproduce results from equivalent simulations run with the existing functionality, but at 2.5 times greater computational efficiency. This new implementation is also able to run softcore simulations at the λ end states making direct calculation of free energies more accurate, compared to the extrapolation required in the existing implementation. The updated alchemical transformation functionality will be included in the next major release of Amber (scheduled for release in Q1 2014) and will be available at http://ambermd.org, under the Amber license.

Improving the Efficiency of Free Energy Calculations in the Amber Molecular Dynamics Package

PubMed Central

Pierce, Levi T.; Walker, Ross C.; McCammont, J. Andrew

2013-01-01

Alchemical transformations are widely used methods to calculate free energies. Amber has traditionally included support for alchemical transformations as part of the sander molecular dynamics (MD) engine. Here we describe the implementation of a more efficient approach to alchemical transformations in the Amber MD package. Specifically we have implemented this new approach within the more computational efficient and scalable pmemd MD engine that is included with the Amber MD package. The majority of the gain in efficiency comes from the improved design of the calculation, which includes better parallel scaling and reduction in the calculation of redundant terms. This new implementation is able to reproduce results from equivalent simulations run with the existing functionality, but at 2.5 times greater computational efficiency. This new implementation is also able to run softcore simulations at the λ end states making direct calculation of free energies more accurate, compared to the extrapolation required in the existing implementation. The updated alchemical transformation functionality will be included in the next major release of Amber (scheduled for release in Q1 2014) and will be available at http://ambermd.org, under the Amber license. PMID:24185531

20-GFLOPS QR processor on a Xilinx Virtex-E FPGA

NASA Astrophysics Data System (ADS)

Walke, Richard L.; Smith, Robert W. M.; Lightbody, Gaye

2000-11-01

Adaptive beamforming can play an important role in sensor array systems in countering directional interference. In high-sample rate systems, such as radar and comms, the calculation of adaptive weights is a very computational task that requires highly parallel solutions. For systems where low power consumption and volume are important the only viable implementation is as an Application Specific Integrated Circuit (ASIC). However, the rapid advancement of Field Programmable Gate Array (FPGA) technology is enabling highly credible re-programmable solutions. In this paper we present the implementation of a scalable linear array processor for weight calculation using QR decomposition. We employ floating-point arithmetic with mantissa size optimized to the target application to minimize component size, and implement them as relationally placed macros (RPMs) on Xilinx Virtex FPGAs to achieve predictable dense layout and high-speed operation. We present results that show that 20GFLOPS of sustained computation on a single XCV3200E-8 Virtex-E FPGA is possible. We also describe the parameterized implementation of the floating-point operators and QR-processor, and the design methodology that enables us to rapidly generate complex FPGA implementations using the industry standard hardware description language VHDL.

Scalable software architecture for on-line multi-camera video processing

NASA Astrophysics Data System (ADS)

Camplani, Massimo; Salgado, Luis

2011-03-01

In this paper we present a scalable software architecture for on-line multi-camera video processing, that guarantees a good trade off between computational power, scalability and flexibility. The software system is modular and its main blocks are the Processing Units (PUs), and the Central Unit. The Central Unit works as a supervisor of the running PUs and each PU manages the acquisition phase and the processing phase. Furthermore, an approach to easily parallelize the desired processing application has been presented. In this paper, as case study, we apply the proposed software architecture to a multi-camera system in order to efficiently manage multiple 2D object detection modules in a real-time scenario. System performance has been evaluated under different load conditions such as number of cameras and image sizes. The results show that the software architecture scales well with the number of camera and can easily works with different image formats respecting the real time constraints. Moreover, the parallelization approach can be used in order to speed up the processing tasks with a low level of overhead.

An integrated semiconductor device enabling non-optical genome sequencing.

PubMed

Rothberg, Jonathan M; Hinz, Wolfgang; Rearick, Todd M; Schultz, Jonathan; Mileski, William; Davey, Mel; Leamon, John H; Johnson, Kim; Milgrew, Mark J; Edwards, Matthew; Hoon, Jeremy; Simons, Jan F; Marran, David; Myers, Jason W; Davidson, John F; Branting, Annika; Nobile, John R; Puc, Bernard P; Light, David; Clark, Travis A; Huber, Martin; Branciforte, Jeffrey T; Stoner, Isaac B; Cawley, Simon E; Lyons, Michael; Fu, Yutao; Homer, Nils; Sedova, Marina; Miao, Xin; Reed, Brian; Sabina, Jeffrey; Feierstein, Erika; Schorn, Michelle; Alanjary, Mohammad; Dimalanta, Eileen; Dressman, Devin; Kasinskas, Rachel; Sokolsky, Tanya; Fidanza, Jacqueline A; Namsaraev, Eugeni; McKernan, Kevin J; Williams, Alan; Roth, G Thomas; Bustillo, James

2011-07-20

The seminal importance of DNA sequencing to the life sciences, biotechnology and medicine has driven the search for more scalable and lower-cost solutions. Here we describe a DNA sequencing technology in which scalable, low-cost semiconductor manufacturing techniques are used to make an integrated circuit able to directly perform non-optical DNA sequencing of genomes. Sequence data are obtained by directly sensing the ions produced by template-directed DNA polymerase synthesis using all-natural nucleotides on this massively parallel semiconductor-sensing device or ion chip. The ion chip contains ion-sensitive, field-effect transistor-based sensors in perfect register with 1.2 million wells, which provide confinement and allow parallel, simultaneous detection of independent sequencing reactions. Use of the most widely used technology for constructing integrated circuits, the complementary metal-oxide semiconductor (CMOS) process, allows for low-cost, large-scale production and scaling of the device to higher densities and larger array sizes. We show the performance of the system by sequencing three bacterial genomes, its robustness and scalability by producing ion chips with up to 10 times as many sensors and sequencing a human genome.

An O(N) and parallel approach to integral problems by a kernel-independent fast multipole method: Application to polarization and magnetization of interacting particles

NASA Astrophysics Data System (ADS)

Jiang, Xikai; Li, Jiyuan; Zhao, Xujun; Qin, Jian; Karpeev, Dmitry; Hernandez-Ortiz, Juan; de Pablo, Juan J.; Heinonen, Olle

2016-08-01

Large classes of materials systems in physics and engineering are governed by magnetic and electrostatic interactions. Continuum or mesoscale descriptions of such systems can be cast in terms of integral equations, whose direct computational evaluation requires O(N2) operations, where N is the number of unknowns. Such a scaling, which arises from the many-body nature of the relevant Green's function, has precluded wide-spread adoption of integral methods for solution of large-scale scientific and engineering problems. In this work, a parallel computational approach is presented that relies on using scalable open source libraries and utilizes a kernel-independent Fast Multipole Method (FMM) to evaluate the integrals in O(N) operations, with O(N) memory cost, thereby substantially improving the scalability and efficiency of computational integral methods. We demonstrate the accuracy, efficiency, and scalability of our approach in the context of two examples. In the first, we solve a boundary value problem for a ferroelectric/ferromagnetic volume in free space. In the second, we solve an electrostatic problem involving polarizable dielectric bodies in an unbounded dielectric medium. The results from these test cases show that our proposed parallel approach, which is built on a kernel-independent FMM, can enable highly efficient and accurate simulations and allow for considerable flexibility in a broad range of applications.

An O( N) and parallel approach to integral problems by a kernel-independent fast multipole method: Application to polarization and magnetization of interacting particles

DOE PAGES

Jiang, Xikai; Li, Jiyuan; Zhao, Xujun; ...

2016-08-10

Large classes of materials systems in physics and engineering are governed by magnetic and electrostatic interactions. Continuum or mesoscale descriptions of such systems can be cast in terms of integral equations, whose direct computational evaluation requires O( N 2) operations, where N is the number of unknowns. Such a scaling, which arises from the many-body nature of the relevant Green's function, has precluded wide-spread adoption of integral methods for solution of large-scale scientific and engineering problems. In this work, a parallel computational approach is presented that relies on using scalable open source libraries and utilizes a kernel-independent Fast Multipole Methodmore » (FMM) to evaluate the integrals in O( N) operations, with O( N) memory cost, thereby substantially improving the scalability and efficiency of computational integral methods. We demonstrate the accuracy, efficiency, and scalability of our approach in the context of two examples. In the first, we solve a boundary value problem for a ferroelectric/ferromagnetic volume in free space. In the second, we solve an electrostatic problem involving polarizable dielectric bodies in an unbounded dielectric medium. Lastly, the results from these test cases show that our proposed parallel approach, which is built on a kernel-independent FMM, can enable highly efficient and accurate simulations and allow for considerable flexibility in a broad range of applications.« less

Automatic Parallelization of Numerical Python Applications using the Global Arrays Toolkit

DOE Office of Scientific and Technical Information (OSTI.GOV)

Daily, Jeffrey A.; Lewis, Robert R.

2011-11-30

Global Arrays is a software system from Pacific Northwest National Laboratory that enables an efficient, portable, and parallel shared-memory programming interface to manipulate distributed dense arrays. The NumPy module is the de facto standard for numerical calculation in the Python programming language, a language whose use is growing rapidly in the scientific and engineering communities. NumPy provides a powerful N-dimensional array class as well as other scientific computing capabilities. However, like the majority of the core Python modules, NumPy is inherently serial. Using a combination of Global Arrays and NumPy, we have reimplemented NumPy as a distributed drop-in replacement calledmore » Global Arrays in NumPy (GAiN). Serial NumPy applications can become parallel, scalable GAiN applications with only minor source code changes. Scalability studies of several different GAiN applications will be presented showing the utility of developing serial NumPy codes which can later run on more capable clusters or supercomputers.« less

High performance data transfer

NASA Astrophysics Data System (ADS)

Cottrell, R.; Fang, C.; Hanushevsky, A.; Kreuger, W.; Yang, W.

2017-10-01

The exponentially increasing need for high speed data transfer is driven by big data, and cloud computing together with the needs of data intensive science, High Performance Computing (HPC), defense, the oil and gas industry etc. We report on the Zettar ZX software. This has been developed since 2013 to meet these growing needs by providing high performance data transfer and encryption in a scalable, balanced, easy to deploy and use way while minimizing power and space utilization. In collaboration with several commercial vendors, Proofs of Concept (PoC) consisting of clusters have been put together using off-the- shelf components to test the ZX scalability and ability to balance services using multiple cores, and links. The PoCs are based on SSD flash storage that is managed by a parallel file system. Each cluster occupies 4 rack units. Using the PoCs, between clusters we have achieved almost 200Gbps memory to memory over two 100Gbps links, and 70Gbps parallel file to parallel file with encryption over a 5000 mile 100Gbps link.

Automated Performance Prediction of Message-Passing Parallel Programs

NASA Technical Reports Server (NTRS)

Block, Robert J.; Sarukkai, Sekhar; Mehra, Pankaj; Woodrow, Thomas S. (Technical Monitor)

1995-01-01

The increasing use of massively parallel supercomputers to solve large-scale scientific problems has generated a need for tools that can predict scalability trends of applications written for these machines. Much work has been done to create simple models that represent important characteristics of parallel programs, such as latency, network contention, and communication volume. But many of these methods still require substantial manual effort to represent an application in the model's format. The NIK toolkit described in this paper is the result of an on-going effort to automate the formation of analytic expressions of program execution time, with a minimum of programmer assistance. In this paper we demonstrate the feasibility of our approach, by extending previous work to detect and model communication patterns automatically, with and without overlapped computations. The predictions derived from these models agree, within reasonable limits, with execution times of programs measured on the Intel iPSC/860 and Paragon. Further, we demonstrate the use of MK in selecting optimal computational grain size and studying various scalability metrics.

ASC-ATDM Performance Portability Requirements for 2015-2019

DOE Office of Scientific and Technical Information (OSTI.GOV)

Edwards, Harold C.; Trott, Christian Robert

This report outlines the research, development, and support requirements for the Advanced Simulation and Computing (ASC ) Advanced Technology, Development, and Mitigation (ATDM) Performance Portability (a.k.a., Kokkos) project for 2015 - 2019 . The research and development (R&D) goal for Kokkos (v2) has been to create and demonstrate a thread - parallel programming model a nd standard C++ library - based implementation that enables performance portability across diverse manycore architectures such as multicore CPU, Intel Xeon Phi, and NVIDIA Kepler GPU. This R&D goal has been achieved for algorithms that use data parallel pat terns including parallel - for, parallelmore » - reduce, and parallel - scan. Current R&D is focusing on hierarchical parallel patterns such as a directed acyclic graph (DAG) of asynchronous tasks where each task contain s nested data parallel algorithms. This five y ear plan includes R&D required to f ully and performance portably exploit thread parallelism across current and anticipated next generation platforms (NGP). The Kokkos library is being evaluated by many projects exploring algorithm s and code design for NGP. Some production libraries and applications such as Trilinos and LAMMPS have already committed to Kokkos as their foundation for manycore parallelism an d performance portability. These five year requirements includes support required for current and antic ipated ASC projects to be effective and productive in their use of Kokkos on NGP. The greatest risk to the success of Kokkos and ASC projects relying upon Kokkos is a lack of staffing resources to support Kokkos to the degree needed by these ASC projects. This support includes up - to - date tutorials, documentation, multi - platform (hardware and software stack) testing, minor feature enhancements, thread - scalable algorithm consulting, and managing collaborative R&D.« less

A high-performance spatial database based approach for pathology imaging algorithm evaluation

PubMed Central

Wang, Fusheng; Kong, Jun; Gao, Jingjing; Cooper, Lee A.D.; Kurc, Tahsin; Zhou, Zhengwen; Adler, David; Vergara-Niedermayr, Cristobal; Katigbak, Bryan; Brat, Daniel J.; Saltz, Joel H.

2013-01-01

Background: Algorithm evaluation provides a means to characterize variability across image analysis algorithms, validate algorithms by comparison with human annotations, combine results from multiple algorithms for performance improvement, and facilitate algorithm sensitivity studies. The sizes of images and image analysis results in pathology image analysis pose significant challenges in algorithm evaluation. We present an efficient parallel spatial database approach to model, normalize, manage, and query large volumes of analytical image result data. This provides an efficient platform for algorithm evaluation. Our experiments with a set of brain tumor images demonstrate the application, scalability, and effectiveness of the platform. Context: The paper describes an approach and platform for evaluation of pathology image analysis algorithms. The platform facilitates algorithm evaluation through a high-performance database built on the Pathology Analytic Imaging Standards (PAIS) data model. Aims: (1) Develop a framework to support algorithm evaluation by modeling and managing analytical results and human annotations from pathology images; (2) Create a robust data normalization tool for converting, validating, and fixing spatial data from algorithm or human annotations; (3) Develop a set of queries to support data sampling and result comparisons; (4) Achieve high performance computation capacity via a parallel data management infrastructure, parallel data loading and spatial indexing optimizations in this infrastructure. Materials and Methods: We have considered two scenarios for algorithm evaluation: (1) algorithm comparison where multiple result sets from different methods are compared and consolidated; and (2) algorithm validation where algorithm results are compared with human annotations. We have developed a spatial normalization toolkit to validate and normalize spatial boundaries produced by image analysis algorithms or human annotations. The validated data were formatted based on the PAIS data model and loaded into a spatial database. To support efficient data loading, we have implemented a parallel data loading tool that takes advantage of multi-core CPUs to accelerate data injection. The spatial database manages both geometric shapes and image features or classifications, and enables spatial sampling, result comparison, and result aggregation through expressive structured query language (SQL) queries with spatial extensions. To provide scalable and efficient query support, we have employed a shared nothing parallel database architecture, which distributes data homogenously across multiple database partitions to take advantage of parallel computation power and implements spatial indexing to achieve high I/O throughput. Results: Our work proposes a high performance, parallel spatial database platform for algorithm validation and comparison. This platform was evaluated by storing, managing, and comparing analysis results from a set of brain tumor whole slide images. The tools we develop are open source and available to download. Conclusions: Pathology image algorithm validation and comparison are essential to iterative algorithm development and refinement. One critical component is the support for queries involving spatial predicates and comparisons. In our work, we develop an efficient data model and parallel database approach to model, normalize, manage and query large volumes of analytical image result data. Our experiments demonstrate that the data partitioning strategy and the grid-based indexing result in good data distribution across database nodes and reduce I/O overhead in spatial join queries through parallel retrieval of relevant data and quick subsetting of datasets. The set of tools in the framework provide a full pipeline to normalize, load, manage and query analytical results for algorithm evaluation. PMID:23599905

High-Speed Computation of the Kleene Star in Max-Plus Algebraic System Using a Cell Broadband Engine

NASA Astrophysics Data System (ADS)

Goto, Hiroyuki

This research addresses a high-speed computation method for the Kleene star of the weighted adjacency matrix in a max-plus algebraic system. We focus on systems whose precedence constraints are represented by a directed acyclic graph and implement it on a Cell Broadband Engine™ (CBE) processor. Since the resulting matrix gives the longest travel times between two adjacent nodes, it is often utilized in scheduling problem solvers for a class of discrete event systems. This research, in particular, attempts to achieve a speedup by using two approaches: parallelization and SIMDization (Single Instruction, Multiple Data), both of which can be accomplished by a CBE processor. The former refers to a parallel computation using multiple cores, while the latter is a method whereby multiple elements are computed by a single instruction. Using the implementation on a Sony PlayStation 3™ equipped with a CBE processor, we found that the SIMDization is effective regardless of the system's size and the number of processor cores used. We also found that the scalability of using multiple cores is remarkable especially for systems with a large number of nodes. In a numerical experiment where the number of nodes is 2000, we achieved a speedup of 20 times compared with the method without the above techniques.

HipMCL: a high-performance parallel implementation of the Markov clustering algorithm for large-scale networks

PubMed Central

Azad, Ariful; Ouzounis, Christos A; Kyrpides, Nikos C; Buluç, Aydin

2018-01-01

Abstract Biological networks capture structural or functional properties of relevant entities such as molecules, proteins or genes. Characteristic examples are gene expression networks or protein–protein interaction networks, which hold information about functional affinities or structural similarities. Such networks have been expanding in size due to increasing scale and abundance of biological data. While various clustering algorithms have been proposed to find highly connected regions, Markov Clustering (MCL) has been one of the most successful approaches to cluster sequence similarity or expression networks. Despite its popularity, MCL’s scalability to cluster large datasets still remains a bottleneck due to high running times and memory demands. Here, we present High-performance MCL (HipMCL), a parallel implementation of the original MCL algorithm that can run on distributed-memory computers. We show that HipMCL can efficiently utilize 2000 compute nodes and cluster a network of ∼70 million nodes with ∼68 billion edges in ∼2.4 h. By exploiting distributed-memory environments, HipMCL clusters large-scale networks several orders of magnitude faster than MCL and enables clustering of even bigger networks. HipMCL is based on MPI and OpenMP and is freely available under a modified BSD license. PMID:29315405

On Multiple AER Handshaking Channels Over High-Speed Bit-Serial Bidirectional LVDS Links With Flow-Control and Clock-Correction on Commercial FPGAs for Scalable Neuromorphic Systems.

PubMed

Yousefzadeh, Amirreza; Jablonski, Miroslaw; Iakymchuk, Taras; Linares-Barranco, Alejandro; Rosado, Alfredo; Plana, Luis A; Temple, Steve; Serrano-Gotarredona, Teresa; Furber, Steve B; Linares-Barranco, Bernabe

2017-10-01

Address event representation (AER) is a widely employed asynchronous technique for interchanging "neural spikes" between different hardware elements in neuromorphic systems. Each neuron or cell in a chip or a system is assigned an address (or ID), which is typically communicated through a high-speed digital bus, thus time-multiplexing a high number of neural connections. Conventional AER links use parallel physical wires together with a pair of handshaking signals (request and acknowledge). In this paper, we present a fully serial implementation using bidirectional SATA connectors with a pair of low-voltage differential signaling (LVDS) wires for each direction. The proposed implementation can multiplex a number of conventional parallel AER links for each physical LVDS connection. It uses flow control, clock correction, and byte alignment techniques to transmit 32-bit address events reliably over multiplexed serial connections. The setup has been tested using commercial Spartan6 FPGAs attaining a maximum event transmission speed of 75 Meps (Mega events per second) for 32-bit events at a line rate of 3.0 Gbps. Full HDL codes (vhdl/verilog) and example demonstration codes for the SpiNNaker platform will be made available.

HipMCL: a high-performance parallel implementation of the Markov clustering algorithm for large-scale networks

DOE PAGES

Azad, Ariful; Pavlopoulos, Georgios A.; Ouzounis, Christos A.; ...

2018-01-05

Biological networks capture structural or functional properties of relevant entities such as molecules, proteins or genes. Characteristic examples are gene expression networks or protein–protein interaction networks, which hold information about functional affinities or structural similarities. Such networks have been expanding in size due to increasing scale and abundance of biological data. While various clustering algorithms have been proposed to find highly connected regions, Markov Clustering (MCL) has been one of the most successful approaches to cluster sequence similarity or expression networks. Despite its popularity, MCL’s scalability to cluster large datasets still remains a bottleneck due to high running times andmore » memory demands. In this paper, we present High-performance MCL (HipMCL), a parallel implementation of the original MCL algorithm that can run on distributed-memory computers. We show that HipMCL can efficiently utilize 2000 compute nodes and cluster a network of ~70 million nodes with ~68 billion edges in ~2.4 h. By exploiting distributed-memory environments, HipMCL clusters large-scale networks several orders of magnitude faster than MCL and enables clustering of even bigger networks. Finally, HipMCL is based on MPI and OpenMP and is freely available under a modified BSD license.« less

HipMCL: a high-performance parallel implementation of the Markov clustering algorithm for large-scale networks

DOE Office of Scientific and Technical Information (OSTI.GOV)

Azad, Ariful; Pavlopoulos, Georgios A.; Ouzounis, Christos A.

Biological networks capture structural or functional properties of relevant entities such as molecules, proteins or genes. Characteristic examples are gene expression networks or protein–protein interaction networks, which hold information about functional affinities or structural similarities. Such networks have been expanding in size due to increasing scale and abundance of biological data. While various clustering algorithms have been proposed to find highly connected regions, Markov Clustering (MCL) has been one of the most successful approaches to cluster sequence similarity or expression networks. Despite its popularity, MCL’s scalability to cluster large datasets still remains a bottleneck due to high running times andmore » memory demands. In this paper, we present High-performance MCL (HipMCL), a parallel implementation of the original MCL algorithm that can run on distributed-memory computers. We show that HipMCL can efficiently utilize 2000 compute nodes and cluster a network of ~70 million nodes with ~68 billion edges in ~2.4 h. By exploiting distributed-memory environments, HipMCL clusters large-scale networks several orders of magnitude faster than MCL and enables clustering of even bigger networks. Finally, HipMCL is based on MPI and OpenMP and is freely available under a modified BSD license.« less

Climate Data Assimilation on a Massively Parallel Supercomputer

NASA Technical Reports Server (NTRS)

Ding, Hong Q.; Ferraro, Robert D.

1996-01-01

We have designed and implemented a set of highly efficient and highly scalable algorithms for an unstructured computational package, the PSAS data assimilation package, as demonstrated by detailed performance analysis of systematic runs on up to 512-nodes of an Intel Paragon. The preconditioned Conjugate Gradient solver achieves a sustained 18 Gflops performance. Consequently, we achieve an unprecedented 100-fold reduction in time to solution on the Intel Paragon over a single head of a Cray C90. This not only exceeds the daily performance requirement of the Data Assimilation Office at NASA's Goddard Space Flight Center, but also makes it possible to explore much larger and challenging data assimilation problems which are unthinkable on a traditional computer platform such as the Cray C90.

SciSpark: Highly Interactive and Scalable Model Evaluation and Climate Metrics

NASA Astrophysics Data System (ADS)

Wilson, B. D.; Mattmann, C. A.; Waliser, D. E.; Kim, J.; Loikith, P.; Lee, H.; McGibbney, L. J.; Whitehall, K. D.

2014-12-01

Remote sensing data and climate model output are multi-dimensional arrays of massive sizes locked away in heterogeneous file formats (HDF5/4, NetCDF 3/4) and metadata models (HDF-EOS, CF) making it difficult to perform multi-stage, iterative science processing since each stage requires writing and reading data to and from disk. We are developing a lightning fast Big Data technology called SciSpark based on ApacheTM Spark. Spark implements the map-reduce paradigm for parallel computing on a cluster, but emphasizes in-memory computation, "spilling" to disk only as needed, and so outperforms the disk-based ApacheTM Hadoop by 100x in memory and by 10x on disk, and makes iterative algorithms feasible. SciSpark will enable scalable model evaluation by executing large-scale comparisons of A-Train satellite observations to model grids on a cluster of 100 to 1000 compute nodes. This 2nd generation capability for NASA's Regional Climate Model Evaluation System (RCMES) will compute simple climate metrics at interactive speeds, and extend to quite sophisticated iterative algorithms such as machine-learning (ML) based clustering of temperature PDFs, and even graph-based algorithms for searching for Mesocale Convective Complexes. The goals of SciSpark are to: (1) Decrease the time to compute comparison statistics and plots from minutes to seconds; (2) Allow for interactive exploration of time-series properties over seasons and years; (3) Decrease the time for satellite data ingestion into RCMES to hours; (4) Allow for Level-2 comparisons with higher-order statistics or PDF's in minutes to hours; and (5) Move RCMES into a near real time decision-making platform. We will report on: the architecture and design of SciSpark, our efforts to integrate climate science algorithms in Python and Scala, parallel ingest and partitioning (sharding) of A-Train satellite observations from HDF files and model grids from netCDF files, first parallel runs to compute comparison statistics and PDF's, and first metrics quantifying parallel speedups and memory & disk usage.

The high performance parallel algorithm for Unified Gas-Kinetic Scheme

NASA Astrophysics Data System (ADS)

Li, Shiyi; Li, Qibing; Fu, Song; Xu, Jinxiu

2016-11-01

A high performance parallel algorithm for UGKS is developed to simulate three-dimensional flows internal and external on arbitrary grid system. The physical domain and velocity domain are divided into different blocks and distributed according to the two-dimensional Cartesian topology with intra-communicators in physical domain for data exchange and other intra-communicators in velocity domain for sum reduction to moment integrals. Numerical results of three-dimensional cavity flow and flow past a sphere agree well with the results from the existing studies and validate the applicability of the algorithm. The scalability of the algorithm is tested both on small (1-16) and large (729-5832) scale processors. The tested speed-up ratio is near linear ashind thus the efficiency is around 1, which reveals the good scalability of the present algorithm.

Microresonator-based solitons for massively parallel coherent optical communications

NASA Astrophysics Data System (ADS)

Marin-Palomo, Pablo; Kemal, Juned N.; Karpov, Maxim; Kordts, Arne; Pfeifle, Joerg; Pfeiffer, Martin H. P.; Trocha, Philipp; Wolf, Stefan; Brasch, Victor; Anderson, Miles H.; Rosenberger, Ralf; Vijayan, Kovendhan; Freude, Wolfgang; Kippenberg, Tobias J.; Koos, Christian

2017-06-01

Solitons are waveforms that preserve their shape while propagating, as a result of a balance of dispersion and nonlinearity. Soliton-based data transmission schemes were investigated in the 1980s and showed promise as a way of overcoming the limitations imposed by dispersion of optical fibres. However, these approaches were later abandoned in favour of wavelength-division multiplexing schemes, which are easier to implement and offer improved scalability to higher data rates. Here we show that solitons could make a comeback in optical communications, not as a competitor but as a key element of massively parallel wavelength-division multiplexing. Instead of encoding data on the soliton pulse train itself, we use continuous-wave tones of the associated frequency comb as carriers for communication. Dissipative Kerr solitons (DKSs) (solitons that rely on a double balance of parametric gain and cavity loss, as well as dispersion and nonlinearity) are generated as continuously circulating pulses in an integrated silicon nitride microresonator via four-photon interactions mediated by the Kerr nonlinearity, leading to low-noise, spectrally smooth, broadband optical frequency combs. We use two interleaved DKS frequency combs to transmit a data stream of more than 50 terabits per second on 179 individual optical carriers that span the entire telecommunication C and L bands (centred around infrared telecommunication wavelengths of 1.55 micrometres). We also demonstrate coherent detection of a wavelength-division multiplexing data stream by using a pair of DKS frequency combs—one as a multi-wavelength light source at the transmitter and the other as the corresponding local oscillator at the receiver. This approach exploits the scalability of microresonator-based DKS frequency comb sources for massively parallel optical communications at both the transmitter and the receiver. Our results demonstrate the potential of these sources to replace the arrays of continuous-wave lasers that are currently used in high-speed communications. In combination with advanced spatial multiplexing schemes and highly integrated silicon photonic circuits, DKS frequency combs could bring chip-scale petabit-per-second transceivers into reach.

A highly scalable particle tracking algorithm using partitioned global address space (PGAS) programming for extreme-scale turbulence simulations

NASA Astrophysics Data System (ADS)

Buaria, D.; Yeung, P. K.

2017-12-01

A new parallel algorithm utilizing a partitioned global address space (PGAS) programming model to achieve high scalability is reported for particle tracking in direct numerical simulations of turbulent fluid flow. The work is motivated by the desire to obtain Lagrangian information necessary for the study of turbulent dispersion at the largest problem sizes feasible on current and next-generation multi-petaflop supercomputers. A large population of fluid particles is distributed among parallel processes dynamically, based on instantaneous particle positions such that all of the interpolation information needed for each particle is available either locally on its host process or neighboring processes holding adjacent sub-domains of the velocity field. With cubic splines as the preferred interpolation method, the new algorithm is designed to minimize the need for communication, by transferring between adjacent processes only those spline coefficients determined to be necessary for specific particles. This transfer is implemented very efficiently as a one-sided communication, using Co-Array Fortran (CAF) features which facilitate small data movements between different local partitions of a large global array. The cost of monitoring transfer of particle properties between adjacent processes for particles migrating across sub-domain boundaries is found to be small. Detailed benchmarks are obtained on the Cray petascale supercomputer Blue Waters at the University of Illinois, Urbana-Champaign. For operations on the particles in a 81923 simulation (0.55 trillion grid points) on 262,144 Cray XE6 cores, the new algorithm is found to be orders of magnitude faster relative to a prior algorithm in which each particle is tracked by the same parallel process at all times. This large speedup reduces the additional cost of tracking of order 300 million particles to just over 50% of the cost of computing the Eulerian velocity field at this scale. Improving support of PGAS models on major compilers suggests that this algorithm will be of wider applicability on most upcoming supercomputers.

3D CSEM inversion based on goal-oriented adaptive finite element method

NASA Astrophysics Data System (ADS)

Zhang, Y.; Key, K.

2016-12-01

We present a parallel 3D frequency domain controlled-source electromagnetic inversion code name MARE3DEM. Non-linear inversion of observed data is performed with the Occam variant of regularized Gauss-Newton optimization. The forward operator is based on the goal-oriented finite element method that efficiently calculates the responses and sensitivity kernels in parallel using a data decomposition scheme where independent modeling tasks contain different frequencies and subsets of the transmitters and receivers. To accommodate complex 3D conductivity variation with high flexibility and precision, we adopt the dual-grid approach where the forward mesh conforms to the inversion parameter grid and is adaptively refined until the forward solution converges to the desired accuracy. This dual-grid approach is memory efficient, since the inverse parameter grid remains independent from fine meshing generated around the transmitter and receivers by the adaptive finite element method. Besides, the unstructured inverse mesh efficiently handles multiple scale structures and allows for fine-scale model parameters within the region of interest. Our mesh generation engine keeps track of the refinement hierarchy so that the map of conductivity and sensitivity kernel between the forward and inverse mesh is retained. We employ the adjoint-reciprocity method to calculate the sensitivity kernels which establish a linear relationship between changes in the conductivity model and changes in the modeled responses. Our code uses a direcy solver for the linear systems, so the adjoint problem is efficiently computed by re-using the factorization from the primary problem. Further computational efficiency and scalability is obtained in the regularized Gauss-Newton portion of the inversion using parallel dense matrix-matrix multiplication and matrix factorization routines implemented with the ScaLAPACK library. We show the scalability, reliability and the potential of the algorithm to deal with complex geological scenarios by applying it to the inversion of synthetic marine controlled source EM data generated for a complex 3D offshore model with significant seafloor topography.

Collective Framework and Performance Optimizations to Open MPI for Cray XT Platforms

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ladd, Joshua S; Gorentla Venkata, Manjunath; Shamis, Pavel

2011-01-01

The performance and scalability of collective operations plays a key role in the performance and scalability of many scientific applications. Within the Open MPI code base we have developed a general purpose hierarchical collective operations framework called Cheetah, and applied it at large scale on the Oak Ridge Leadership Computing Facility's Jaguar (OLCF) platform, obtaining better performance and scalability than the native MPI implementation. This paper discuss Cheetah's design and implementation, and optimizations to the framework for Cray XT 5 platforms. Our results show that the Cheetah's Broadcast and Barrier perform better than the native MPI implementation. For medium data,more » the Cheetah's Broadcast outperforms the native MPI implementation by 93% for 49,152 processes problem size. For small and large data, it out performs the native MPI implementation by 10% and 9%, respectively, at 24,576 processes problem size. The Cheetah's Barrier performs 10% better than the native MPI implementation for 12,288 processes problem size.« less

A Scalable O(N) Algorithm for Large-Scale Parallel First-Principles Molecular Dynamics Simulations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Osei-Kuffuor, Daniel; Fattebert, Jean-Luc

2014-01-01

Traditional algorithms for first-principles molecular dynamics (FPMD) simulations only gain a modest capability increase from current petascale computers, due to their O(N 3) complexity and their heavy use of global communications. To address this issue, we are developing a truly scalable O(N) complexity FPMD algorithm, based on density functional theory (DFT), which avoids global communications. The computational model uses a general nonorthogonal orbital formulation for the DFT energy functional, which requires knowledge of selected elements of the inverse of the associated overlap matrix. We present a scalable algorithm for approximately computing selected entries of the inverse of the overlap matrix,more » based on an approximate inverse technique, by inverting local blocks corresponding to principal submatrices of the global overlap matrix. The new FPMD algorithm exploits sparsity and uses nearest neighbor communication to provide a computational scheme capable of extreme scalability. Accuracy is controlled by the mesh spacing of the finite difference discretization, the size of the localization regions in which the electronic orbitals are confined, and a cutoff beyond which the entries of the overlap matrix can be omitted when computing selected entries of its inverse. We demonstrate the algorithm's excellent parallel scaling for up to O(100K) atoms on O(100K) processors, with a wall-clock time of O(1) minute per molecular dynamics time step.« less

Scalable improvement of SPME multipolar electrostatics in anisotropic polarizable molecular mechanics using a general short-range penetration correction up to quadrupoles.

PubMed

Narth, Christophe; Lagardère, Louis; Polack, Étienne; Gresh, Nohad; Wang, Qiantao; Bell, David R; Rackers, Joshua A; Ponder, Jay W; Ren, Pengyu Y; Piquemal, Jean-Philip

2016-02-15

We propose a general coupling of the Smooth Particle Mesh Ewald SPME approach for distributed multipoles to a short-range charge penetration correction modifying the charge-charge, charge-dipole and charge-quadrupole energies. Such an approach significantly improves electrostatics when compared to ab initio values and has been calibrated on Symmetry-Adapted Perturbation Theory reference data. Various neutral molecular dimers have been tested and results on the complexes of mono- and divalent cations with a water ligand are also provided. Transferability of the correction is adressed in the context of the implementation of the AMOEBA and SIBFA polarizable force fields in the TINKER-HP software. As the choices of the multipolar distribution are discussed, conclusions are drawn for the future penetration-corrected polarizable force fields highlighting the mandatory need of non-spurious procedures for the obtention of well balanced and physically meaningful distributed moments. Finally, scalability and parallelism of the short-range corrected SPME approach are addressed, demonstrating that the damping function is computationally affordable and accurate for molecular dynamics simulations of complex bio- or bioinorganic systems in periodic boundary conditions. Copyright © 2016 Wiley Periodicals, Inc.

Scalable and Interactive Segmentation and Visualization of Neural Processes in EM Datasets

PubMed Central

Jeong, Won-Ki; Beyer, Johanna; Hadwiger, Markus; Vazquez, Amelio; Pfister, Hanspeter; Whitaker, Ross T.

2011-01-01

Recent advances in scanning technology provide high resolution EM (Electron Microscopy) datasets that allow neuroscientists to reconstruct complex neural connections in a nervous system. However, due to the enormous size and complexity of the resulting data, segmentation and visualization of neural processes in EM data is usually a difficult and very time-consuming task. In this paper, we present NeuroTrace, a novel EM volume segmentation and visualization system that consists of two parts: a semi-automatic multiphase level set segmentation with 3D tracking for reconstruction of neural processes, and a specialized volume rendering approach for visualization of EM volumes. It employs view-dependent on-demand filtering and evaluation of a local histogram edge metric, as well as on-the-fly interpolation and ray-casting of implicit surfaces for segmented neural structures. Both methods are implemented on the GPU for interactive performance. NeuroTrace is designed to be scalable to large datasets and data-parallel hardware architectures. A comparison of NeuroTrace with a commonly used manual EM segmentation tool shows that our interactive workflow is faster and easier to use for the reconstruction of complex neural processes. PMID:19834227

Extreme-Scale Bayesian Inference for Uncertainty Quantification of Complex Simulations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Biros, George

Uncertainty quantification (UQ)—that is, quantifying uncertainties in complex mathematical models and their large-scale computational implementations—is widely viewed as one of the outstanding challenges facing the field of CS&E over the coming decade. The EUREKA project set to address the most difficult class of UQ problems: those for which both the underlying PDE model as well as the uncertain parameters are of extreme scale. In the project we worked on these extreme-scale challenges in the following four areas: 1. Scalable parallel algorithms for sampling and characterizing the posterior distribution that exploit the structure of the underlying PDEs and parameter-to-observable map. Thesemore » include structure-exploiting versions of the randomized maximum likelihood method, which aims to overcome the intractability of employing conventional MCMC methods for solving extreme-scale Bayesian inversion problems by appealing to and adapting ideas from large-scale PDE-constrained optimization, which have been very successful at exploring high-dimensional spaces. 2. Scalable parallel algorithms for construction of prior and likelihood functions based on learning methods and non-parametric density estimation. Constructing problem-specific priors remains a critical challenge in Bayesian inference, and more so in high dimensions. Another challenge is construction of likelihood functions that capture unmodeled couplings between observations and parameters. We will create parallel algorithms for non-parametric density estimation using high dimensional N-body methods and combine them with supervised learning techniques for the construction of priors and likelihood functions. 3. Bayesian inadequacy models, which augment physics models with stochastic models that represent their imperfections. The success of the Bayesian inference framework depends on the ability to represent the uncertainty due to imperfections of the mathematical model of the phenomena of interest. This is a central challenge in UQ, especially for large-scale models. We propose to develop the mathematical tools to address these challenges in the context of extreme-scale problems. 4. Parallel scalable algorithms for Bayesian optimal experimental design (OED). Bayesian inversion yields quantified uncertainties in the model parameters, which can be propagated forward through the model to yield uncertainty in outputs of interest. This opens the way for designing new experiments to reduce the uncertainties in the model parameters and model predictions. Such experimental design problems have been intractable for large-scale problems using conventional methods; we will create OED algorithms that exploit the structure of the PDE model and the parameter-to-output map to overcome these challenges. Parallel algorithms for these four problems were created, analyzed, prototyped, implemented, tuned, and scaled up for leading-edge supercomputers, including UT-Austin’s own 10 petaflops Stampede system, ANL’s Mira system, and ORNL’s Titan system. While our focus is on fundamental mathematical/computational methods and algorithms, we will assess our methods on model problems derived from several DOE mission applications, including multiscale mechanics and ice sheet dynamics.« less

DInSAR time series generation within a cloud computing environment: from ERS to Sentinel-1 scenario

NASA Astrophysics Data System (ADS)

Casu, Francesco; Elefante, Stefano; Imperatore, Pasquale; Lanari, Riccardo; Manunta, Michele; Zinno, Ivana; Mathot, Emmanuel; Brito, Fabrice; Farres, Jordi; Lengert, Wolfgang

2013-04-01

One of the techniques that will strongly benefit from the advent of the Sentinel-1 system is Differential SAR Interferometry (DInSAR), which has successfully demonstrated to be an effective tool to detect and monitor ground displacements with centimetre accuracy. The geoscience communities (volcanology, seismicity, …), as well as those related to hazard monitoring and risk mitigation, make extensively use of the DInSAR technique and they will take advantage from the huge amount of SAR data acquired by Sentinel-1. Indeed, such an information will successfully permit the generation of Earth's surface displacement maps and time series both over large areas and long time span. However, the issue of managing, processing and analysing the large Sentinel data stream is envisaged by the scientific community to be a major bottleneck, particularly during crisis phases. The emerging need of creating a common ecosystem in which data, results and processing tools are shared, is envisaged to be a successful way to address such a problem and to contribute to the information and knowledge spreading. The Supersites initiative as well as the ESA SuperSites Exploitation Platform (SSEP) and the ESA Cloud Computing Operational Pilot (CIOP) projects provide effective answers to this need and they are pushing towards the development of such an ecosystem. It is clear that all the current and existent tools for querying, processing and analysing SAR data are required to be not only updated for managing the large data stream of Sentinel-1 satellite, but also reorganized for quickly replying to the simultaneous and highly demanding user requests, mainly during emergency situations. This translates into the automatic and unsupervised processing of large amount of data as well as the availability of scalable, widely accessible and high performance computing capabilities. The cloud computing environment permits to achieve all of these objectives, particularly in case of spike and peak requests of processing resources linked to disaster events. This work aims at presenting a parallel computational model for the widely used DInSAR algorithm named as Small BAseline Subset (SBAS), which has been implemented within the cloud computing environment provided by the ESA-CIOP platform. This activity has resulted in developing a scalable, unsupervised, portable, and widely accessible (through a web portal) parallel DInSAR computational tool. The activity has rewritten and developed the SBAS application algorithm within a parallel system environment, i.e., in a form that allows us to benefit from multiple processing units. This requires the devising a parallel version of the SBAS algorithm and its subsequent implementation, implying additional complexity in algorithm designing and an efficient multi processor programming, with the final aim of a parallel performance optimization. Although the presented algorithm has been designed to work with Sentinel-1 data, it can also process other satellite SAR data (ERS, ENVISAT, CSK, TSX, ALOS). Indeed, the performance analysis of the implemented SBAS parallel version has been tested on the full ASAR archive (64 acquisitions) acquired over the Napoli Bay, a volcanic and densely urbanized area in Southern Italy. The full processing - from the raw data download to the generation of DInSAR time series - has been carried out by engaging 4 nodes, each one with 2 cores and 16 GB of RAM, and has taken about 36 hours, with respect to about 135 hours of the sequential version. Extensive analysis on other test areas significant from DInSAR and geophysical viewpoint will be presented. Finally, preliminary performance evaluation of the presented approach within the Sentinel-1 scenario will be provided.

Long-range interactions and parallel scalability in molecular simulations

NASA Astrophysics Data System (ADS)

Patra, Michael; Hyvönen, Marja T.; Falck, Emma; Sabouri-Ghomi, Mohsen; Vattulainen, Ilpo; Karttunen, Mikko

2007-01-01

Typical biomolecular systems such as cellular membranes, DNA, and protein complexes are highly charged. Thus, efficient and accurate treatment of electrostatic interactions is of great importance in computational modeling of such systems. We have employed the GROMACS simulation package to perform extensive benchmarking of different commonly used electrostatic schemes on a range of computer architectures (Pentium-4, IBM Power 4, and Apple/IBM G5) for single processor and parallel performance up to 8 nodes—we have also tested the scalability on four different networks, namely Infiniband, GigaBit Ethernet, Fast Ethernet, and nearly uniform memory architecture, i.e. communication between CPUs is possible by directly reading from or writing to other CPUs' local memory. It turns out that the particle-mesh Ewald method (PME) performs surprisingly well and offers competitive performance unless parallel runs on PC hardware with older network infrastructure are needed. Lipid bilayers of sizes 128, 512 and 2048 lipid molecules were used as the test systems representing typical cases encountered in biomolecular simulations. Our results enable an accurate prediction of computational speed on most current computing systems, both for serial and parallel runs. These results should be helpful in, for example, choosing the most suitable configuration for a small departmental computer cluster.

Discrete Event Modeling and Massively Parallel Execution of Epidemic Outbreak Phenomena

DOE Office of Scientific and Technical Information (OSTI.GOV)

Perumalla, Kalyan S; Seal, Sudip K

2011-01-01

In complex phenomena such as epidemiological outbreaks, the intensity of inherent feedback effects and the significant role of transients in the dynamics make simulation the only effective method for proactive, reactive or post-facto analysis. The spatial scale, runtime speed, and behavioral detail needed in detailed simulations of epidemic outbreaks make it necessary to use large-scale parallel processing. Here, an optimistic parallel execution of a new discrete event formulation of a reaction-diffusion simulation model of epidemic propagation is presented to facilitate in dramatically increasing the fidelity and speed by which epidemiological simulations can be performed. Rollback support needed during optimistic parallelmore » execution is achieved by combining reverse computation with a small amount of incremental state saving. Parallel speedup of over 5,500 and other runtime performance metrics of the system are observed with weak-scaling execution on a small (8,192-core) Blue Gene / P system, while scalability with a weak-scaling speedup of over 10,000 is demonstrated on 65,536 cores of a large Cray XT5 system. Scenarios representing large population sizes exceeding several hundreds of millions of individuals in the largest cases are successfully exercised to verify model scalability.« less

FWT2D: A massively parallel program for frequency-domain full-waveform tomography of wide-aperture seismic data—Part 1: Algorithm

NASA Astrophysics Data System (ADS)

Sourbier, Florent; Operto, Stéphane; Virieux, Jean; Amestoy, Patrick; L'Excellent, Jean-Yves

2009-03-01

This is the first paper in a two-part series that describes a massively parallel code that performs 2D frequency-domain full-waveform inversion of wide-aperture seismic data for imaging complex structures. Full-waveform inversion methods, namely quantitative seismic imaging methods based on the resolution of the full wave equation, are computationally expensive. Therefore, designing efficient algorithms which take advantage of parallel computing facilities is critical for the appraisal of these approaches when applied to representative case studies and for further improvements. Full-waveform modelling requires the resolution of a large sparse system of linear equations which is performed with the massively parallel direct solver MUMPS for efficient multiple-shot simulations. Efficiency of the multiple-shot solution phase (forward/backward substitutions) is improved by using the BLAS3 library. The inverse problem relies on a classic local optimization approach implemented with a gradient method. The direct solver returns the multiple-shot wavefield solutions distributed over the processors according to a domain decomposition driven by the distribution of the LU factors. The domain decomposition of the wavefield solutions is used to compute in parallel the gradient of the objective function and the diagonal Hessian, this latter providing a suitable scaling of the gradient. The algorithm allows one to test different strategies for multiscale frequency inversion ranging from successive mono-frequency inversion to simultaneous multifrequency inversion. These different inversion strategies will be illustrated in the following companion paper. The parallel efficiency and the scalability of the code will also be quantified.

A feasibility study on porting the community land model onto accelerators using OpenACC

DOE PAGES

Wang, Dali; Wu, Wei; Winkler, Frank; ...

2014-01-01

As environmental models (such as Accelerated Climate Model for Energy (ACME), Parallel Reactive Flow and Transport Model (PFLOTRAN), Arctic Terrestrial Simulator (ATS), etc.) became more and more complicated, we are facing enormous challenges regarding to porting those applications onto hybrid computing architecture. OpenACC appears as a very promising technology, therefore, we have conducted a feasibility analysis on porting the Community Land Model (CLM), a terrestrial ecosystem model within the Community Earth System Models (CESM)). Specifically, we used automatic function testing platform to extract a small computing kernel out of CLM, then we apply this kernel into the actually CLM dataflowmore » procedure, and investigate the strategy of data parallelization and the benefit of data movement provided by current implementation of OpenACC. Even it is a non-intensive kernel, on a single 16-core computing node, the performance (based on the actual computation time using one GPU) of OpenACC implementation is 2.3 time faster than that of OpenMP implementation using single OpenMP thread, but it is 2.8 times slower than the performance of OpenMP implementation using 16 threads. On multiple nodes, MPI_OpenACC implementation demonstrated very good scalability on up to 128 GPUs on 128 computing nodes. This study also provides useful information for us to look into the potential benefits of “deep copy” capability and “routine” feature of OpenACC standards. In conclusion, we believe that our experience on the environmental model, CLM, can be beneficial to many other scientific research programs who are interested to porting their large scale scientific code using OpenACC onto high-end computers, empowered by hybrid computing architecture.« less

Scalable Molecular Dynamics with NAMD

PubMed Central

Phillips, James C.; Braun, Rosemary; Wang, Wei; Gumbart, James; Tajkhorshid, Emad; Villa, Elizabeth; Chipot, Christophe; Skeel, Robert D.; Kalé, Laxmikant; Schulten, Klaus

2008-01-01

NAMD is a parallel molecular dynamics code designed for high-performance simulation of large biomolecular systems. NAMD scales to hundreds of processors on high-end parallel platforms, as well as tens of processors on low-cost commodity clusters, and also runs on individual desktop and laptop computers. NAMD works with AMBER and CHARMM potential functions, parameters, and file formats. This paper, directed to novices as well as experts, first introduces concepts and methods used in the NAMD program, describing the classical molecular dynamics force field, equations of motion, and integration methods along with the efficient electrostatics evaluation algorithms employed and temperature and pressure controls used. Features for steering the simulation across barriers and for calculating both alchemical and conformational free energy differences are presented. The motivations for and a roadmap to the internal design of NAMD, implemented in C++ and based on Charm++ parallel objects, are outlined. The factors affecting the serial and parallel performance of a simulation are discussed. Next, typical NAMD use is illustrated with representative applications to a small, a medium, and a large biomolecular system, highlighting particular features of NAMD, e.g., the Tcl scripting language. Finally, the paper provides a list of the key features of NAMD and discusses the benefits of combining NAMD with the molecular graphics/sequence analysis software VMD and the grid computing/collaboratory software BioCoRE. NAMD is distributed free of charge with source code at www.ks.uiuc.edu. PMID:16222654

Parareal in time 3D numerical solver for the LWR Benchmark neutron diffusion transient model

DOE Office of Scientific and Technical Information (OSTI.GOV)

Baudron, Anne-Marie, E-mail: anne-marie.baudron@cea.fr; CEA-DRN/DMT/SERMA, CEN-Saclay, 91191 Gif sur Yvette Cedex; Lautard, Jean-Jacques, E-mail: jean-jacques.lautard@cea.fr

2014-12-15

In this paper we present a time-parallel algorithm for the 3D neutrons calculation of a transient model in a nuclear reactor core. The neutrons calculation consists in numerically solving the time dependent diffusion approximation equation, which is a simplified transport equation. The numerical resolution is done with finite elements method based on a tetrahedral meshing of the computational domain, representing the reactor core, and time discretization is achieved using a θ-scheme. The transient model presents moving control rods during the time of the reaction. Therefore, cross-sections (piecewise constants) are taken into account by interpolations with respect to the velocity ofmore » the control rods. The parallelism across the time is achieved by an adequate use of the parareal in time algorithm to the handled problem. This parallel method is a predictor corrector scheme that iteratively combines the use of two kinds of numerical propagators, one coarse and one fine. Our method is made efficient by means of a coarse solver defined with large time step and fixed position control rods model, while the fine propagator is assumed to be a high order numerical approximation of the full model. The parallel implementation of our method provides a good scalability of the algorithm. Numerical results show the efficiency of the parareal method on large light water reactor transient model corresponding to the Langenbuch–Maurer–Werner benchmark.« less

Development and Applications of a Modular Parallel Process for Large Scale Fluid/Structures Problems

NASA Technical Reports Server (NTRS)

Guruswamy, Guru P.; Kwak, Dochan (Technical Monitor)

2002-01-01

A modular process that can efficiently solve large scale multidisciplinary problems using massively parallel supercomputers is presented. The process integrates disciplines with diverse physical characteristics by retaining the efficiency of individual disciplines. Computational domain independence of individual disciplines is maintained using a meta programming approach. The process integrates disciplines without affecting the combined performance. Results are demonstrated for large scale aerospace problems on several supercomputers. The super scalability and portability of the approach is demonstrated on several parallel computers.

Development and Applications of a Modular Parallel Process for Large Scale Fluid/Structures Problems

NASA Technical Reports Server (NTRS)

Guruswamy, Guru P.; Byun, Chansup; Kwak, Dochan (Technical Monitor)

2001-01-01

A modular process that can efficiently solve large scale multidisciplinary problems using massively parallel super computers is presented. The process integrates disciplines with diverse physical characteristics by retaining the efficiency of individual disciplines. Computational domain independence of individual disciplines is maintained using a meta programming approach. The process integrates disciplines without affecting the combined performance. Results are demonstrated for large scale aerospace problems on several supercomputers. The super scalability and portability of the approach is demonstrated on several parallel computers.

Integration experiences and performance studies of A COTS parallel archive systems

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chen, Hsing-bung; Scott, Cody; Grider, Bary

2010-01-01

Current and future Archive Storage Systems have been asked to (a) scale to very high bandwidths, (b) scale in metadata performance, (c) support policy-based hierarchical storage management capability, (d) scale in supporting changing needs of very large data sets, (e) support standard interface, and (f) utilize commercial-off-the-shelf(COTS) hardware. Parallel file systems have been asked to do the same thing but at one or more orders of magnitude faster in performance. Archive systems continue to move closer to file systems in their design due to the need for speed and bandwidth, especially metadata searching speeds such as more caching and lessmore » robust semantics. Currently the number of extreme highly scalable parallel archive solutions is very small especially those that will move a single large striped parallel disk file onto many tapes in parallel. We believe that a hybrid storage approach of using COTS components and innovative software technology can bring new capabilities into a production environment for the HPC community much faster than the approach of creating and maintaining a complete end-to-end unique parallel archive software solution. In this paper, we relay our experience of integrating a global parallel file system and a standard backup/archive product with a very small amount of additional code to provide a scalable, parallel archive. Our solution has a high degree of overlap with current parallel archive products including (a) doing parallel movement to/from tape for a single large parallel file, (b) hierarchical storage management, (c) ILM features, (d) high volume (non-single parallel file) archives for backup/archive/content management, and (e) leveraging all free file movement tools in Linux such as copy, move, ls, tar, etc. We have successfully applied our working COTS Parallel Archive System to the current world's first petaflop/s computing system, LANL's Roadrunner, and demonstrated its capability to address requirements of future archival storage systems.« less

Integration experiments and performance studies of a COTS parallel archive system

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chen, Hsing-bung; Scott, Cody; Grider, Gary

2010-06-16

Current and future Archive Storage Systems have been asked to (a) scale to very high bandwidths, (b) scale in metadata performance, (c) support policy-based hierarchical storage management capability, (d) scale in supporting changing needs of very large data sets, (e) support standard interface, and (f) utilize commercial-off-the-shelf (COTS) hardware. Parallel file systems have been asked to do the same thing but at one or more orders of magnitude faster in performance. Archive systems continue to move closer to file systems in their design due to the need for speed and bandwidth, especially metadata searching speeds such as more caching andmore » less robust semantics. Currently the number of extreme highly scalable parallel archive solutions is very small especially those that will move a single large striped parallel disk file onto many tapes in parallel. We believe that a hybrid storage approach of using COTS components and innovative software technology can bring new capabilities into a production environment for the HPC community much faster than the approach of creating and maintaining a complete end-to-end unique parallel archive software solution. In this paper, we relay our experience of integrating a global parallel file system and a standard backup/archive product with a very small amount of additional code to provide a scalable, parallel archive. Our solution has a high degree of overlap with current parallel archive products including (a) doing parallel movement to/from tape for a single large parallel file, (b) hierarchical storage management, (c) ILM features, (d) high volume (non-single parallel file) archives for backup/archive/content management, and (e) leveraging all free file movement tools in Linux such as copy, move, Is, tar, etc. We have successfully applied our working COTS Parallel Archive System to the current world's first petafiop/s computing system, LANL's Roadrunner machine, and demonstrated its capability to address requirements of future archival storage systems.« less

Digital algorithms for parallel pipelined single-detector homodyne fringe counting in laser interferometry

NASA Astrophysics Data System (ADS)

Rerucha, Simon; Sarbort, Martin; Hola, Miroslava; Cizek, Martin; Hucl, Vaclav; Cip, Ondrej; Lazar, Josef

2016-12-01

The homodyne detection with only a single detector represents a promising approach in the interferometric application which enables a significant reduction of the optical system complexity while preserving the fundamental resolution and dynamic range of the single frequency laser interferometers. We present the design, implementation and analysis of algorithmic methods for computational processing of the single-detector interference signal based on parallel pipelined processing suitable for real time implementation on a programmable hardware platform (e.g. the FPGA - Field Programmable Gate Arrays or the SoC - System on Chip). The algorithmic methods incorporate (a) the single detector signal (sine) scaling, filtering, demodulations and mixing necessary for the second (cosine) quadrature signal reconstruction followed by a conic section projection in Cartesian plane as well as (a) the phase unwrapping together with the goniometric and linear transformations needed for the scale linearization and periodic error correction. The digital computing scheme was designed for bandwidths up to tens of megahertz which would allow to measure the displacements at the velocities around half metre per second. The algorithmic methods were tested in real-time operation with a PC-based reference implementation that employed the advantage pipelined processing by balancing the computational load among multiple processor cores. The results indicate that the algorithmic methods are suitable for a wide range of applications [3] and that they are bringing the fringe counting interferometry closer to the industrial applications due to their optical setup simplicity and robustness, computational stability, scalability and also a cost-effectiveness.

Implementation of extended Lagrangian dynamics in GROMACS for polarizable simulations using the classical Drude oscillator model.

PubMed

Lemkul, Justin A; Roux, Benoît; van der Spoel, David; MacKerell, Alexander D

2015-07-15

Explicit treatment of electronic polarization in empirical force fields used for molecular dynamics simulations represents an important advancement in simulation methodology. A straightforward means of treating electronic polarization in these simulations is the inclusion of Drude oscillators, which are auxiliary, charge-carrying particles bonded to the cores of atoms in the system. The additional degrees of freedom make these simulations more computationally expensive relative to simulations using traditional fixed-charge (additive) force fields. Thus, efficient tools are needed for conducting these simulations. Here, we present the implementation of highly scalable algorithms in the GROMACS simulation package that allow for the simulation of polarizable systems using extended Lagrangian dynamics with a dual Nosé-Hoover thermostat as well as simulations using a full self-consistent field treatment of polarization. The performance of systems of varying size is evaluated, showing that the present code parallelizes efficiently and is the fastest implementation of the extended Lagrangian methods currently available for simulations using the Drude polarizable force field. © 2015 Wiley Periodicals, Inc.

Astronomy In The Cloud: Using Mapreduce For Image Coaddition

NASA Astrophysics Data System (ADS)

Wiley, Keith; Connolly, A.; Gardner, J.; Krughoff, S.; Balazinska, M.; Howe, B.; Kwon, Y.; Bu, Y.

2011-01-01

In the coming decade, astronomical surveys of the sky will generate tens of terabytes of images and detect hundreds of millions of sources every night. The study of these sources will involve computational challenges such as anomaly detection, classification, and moving object tracking. Since such studies require the highest quality data, methods such as image coaddition, i.e., registration, stacking, and mosaicing, will be critical to scientific investigation. With a requirement that these images be analyzed on a nightly basis to identify moving sources, e.g., asteroids, or transient objects, e.g., supernovae, these datastreams present many computational challenges. Given the quantity of data involved, the computational load of these problems can only be addressed by distributing the workload over a large number of nodes. However, the high data throughput demanded by these applications may present scalability challenges for certain storage architectures. One scalable data-processing method that has emerged in recent years is MapReduce, and in this paper we focus on its popular open-source implementation called Hadoop. In the Hadoop framework, the data is partitioned among storage attached directly to worker nodes, and the processing workload is scheduled in parallel on the nodes that contain the required input data. A further motivation for using Hadoop is that it allows us to exploit cloud computing resources, i.e., platforms where Hadoop is offered as a service. We report on our experience implementing a scalable image-processing pipeline for the SDSS imaging database using Hadoop. This multi-terabyte imaging dataset provides a good testbed for algorithm development since its scope and structure approximate future surveys. First, we describe MapReduce and how we adapted image coaddition to the MapReduce framework. Then we describe a number of optimizations to our basic approach and report experimental results compring their performance. This work is funded by the NSF and by NASA.

Astronomy in the Cloud: Using MapReduce for Image Co-Addition

NASA Astrophysics Data System (ADS)

Wiley, K.; Connolly, A.; Gardner, J.; Krughoff, S.; Balazinska, M.; Howe, B.; Kwon, Y.; Bu, Y.

2011-03-01

In the coming decade, astronomical surveys of the sky will generate tens of terabytes of images and detect hundreds of millions of sources every night. The study of these sources will involve computation challenges such as anomaly detection and classification and moving-object tracking. Since such studies benefit from the highest-quality data, methods such as image co-addition, i.e., astrometric registration followed by per-pixel summation, will be a critical preprocessing step prior to scientific investigation. With a requirement that these images be analyzed on a nightly basis to identify moving sources such as potentially hazardous asteroids or transient objects such as supernovae, these data streams present many computational challenges. Given the quantity of data involved, the computational load of these problems can only be addressed by distributing the workload over a large number of nodes. However, the high data throughput demanded by these applications may present scalability challenges for certain storage architectures. One scalable data-processing method that has emerged in recent years is MapReduce, and in this article we focus on its popular open-source implementation called Hadoop. In the Hadoop framework, the data are partitioned among storage attached directly to worker nodes, and the processing workload is scheduled in parallel on the nodes that contain the required input data. A further motivation for using Hadoop is that it allows us to exploit cloud computing resources: i.e., platforms where Hadoop is offered as a service. We report on our experience of implementing a scalable image-processing pipeline for the SDSS imaging database using Hadoop. This multiterabyte imaging data set provides a good testbed for algorithm development, since its scope and structure approximate future surveys. First, we describe MapReduce and how we adapted image co-addition to the MapReduce framework. Then we describe a number of optimizations to our basic approach and report experimental results comparing their performance.

Parallel Unsteady Overset Mesh Methodology for a Multi-Solver Paradigm with Adaptive Cartesian Grids

DTIC Science & Technology

2008-08-21

Engineer, U.S. Army Research Laboratory ., Matthew.W.Floros@nasa.gov, AIAA Member ‡Senior Research Scientist, Scaled Numerical Physics LLC., awissink...IV.E and IV.D). Good linear scalability was observed for all three cases up to 12 processors. Beyond that the scalability drops off depending on grid...Research Laboratory for the usage of SUGGAR module and Yikloon Lee at NAVAIR for the usage of the NAVAIR-IHC code. 13 of 22 American Institute of

NWChem: A comprehensive and scalable open-source solution for large scale molecular simulations

NASA Astrophysics Data System (ADS)

Valiev, M.; Bylaska, E. J.; Govind, N.; Kowalski, K.; Straatsma, T. P.; Van Dam, H. J. J.; Wang, D.; Nieplocha, J.; Apra, E.; Windus, T. L.; de Jong, W. A.

2010-09-01

The latest release of NWChem delivers an open-source computational chemistry package with extensive capabilities for large scale simulations of chemical and biological systems. Utilizing a common computational framework, diverse theoretical descriptions can be used to provide the best solution for a given scientific problem. Scalable parallel implementations and modular software design enable efficient utilization of current computational architectures. This paper provides an overview of NWChem focusing primarily on the core theoretical modules provided by the code and their parallel performance. Program summaryProgram title: NWChem Catalogue identifier: AEGI_v1_0 Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AEGI_v1_0.html Program obtainable from: CPC Program Library, Queen's University, Belfast, N. Ireland Licensing provisions: Open Source Educational Community License No. of lines in distributed program, including test data, etc.: 11 709 543 No. of bytes in distributed program, including test data, etc.: 680 696 106 Distribution format: tar.gz Programming language: Fortran 77, C Computer: all Linux based workstations and parallel supercomputers, Windows and Apple machines Operating system: Linux, OS X, Windows Has the code been vectorised or parallelized?: Code is parallelized Classification: 2.1, 2.2, 3, 7.3, 7.7, 16.1, 16.2, 16.3, 16.10, 16.13 Nature of problem: Large-scale atomistic simulations of chemical and biological systems require efficient and reliable methods for ground and excited solutions of many-electron Hamiltonian, analysis of the potential energy surface, and dynamics. Solution method: Ground and excited solutions of many-electron Hamiltonian are obtained utilizing density-functional theory, many-body perturbation approach, and coupled cluster expansion. These solutions or a combination thereof with classical descriptions are then used to analyze potential energy surface and perform dynamical simulations. Additional comments: Full documentation is provided in the distribution file. This includes an INSTALL file giving details of how to build the package. A set of test runs is provided in the examples directory. The distribution file for this program is over 90 Mbytes and therefore is not delivered directly when download or Email is requested. Instead a html file giving details of how the program can be obtained is sent. Running time: Running time depends on the size of the chemical system, complexity of the method, number of cpu's and the computational task. It ranges from several seconds for serial DFT energy calculations on a few atoms to several hours for parallel coupled cluster energy calculations on tens of atoms or ab-initio molecular dynamics simulation on hundreds of atoms.

On implementing clinical decision support: achieving scalability and maintainability by combining business rules and ontologies.

PubMed

Kashyap, Vipul; Morales, Alfredo; Hongsermeier, Tonya

2006-01-01

We present an approach and architecture for implementing scalable and maintainable clinical decision support at the Partners HealthCare System. The architecture integrates a business rules engine that executes declarative if-then rules stored in a rule-base referencing objects and methods in a business object model. The rules engine executes object methods by invoking services implemented on the clinical data repository. Specialized inferences that support classification of data and instances into classes are identified and an approach to implement these inferences using an OWL based ontology engine is presented. Alternative representations of these specialized inferences as if-then rules or OWL axioms are explored and their impact on the scalability and maintenance of the system is presented. Architectural alternatives for integration of clinical decision support functionality with the invoking application and the underlying clinical data repository; and their associated trade-offs are discussed and presented.

A novel processing platform for post tape out flows

NASA Astrophysics Data System (ADS)

Vu, Hien T.; Kim, Soohong; Word, James; Cai, Lynn Y.

2018-03-01

As the computational requirements for post tape out (PTO) flows increase at the 7nm and below technology nodes, there is a need to increase the scalability of the computational tools in order to reduce the turn-around time (TAT) of the flows. Utilization of design hierarchy has been one proven method to provide sufficient partitioning to enable PTO processing. However, as the data is processed through the PTO flow, its effective hierarchy is reduced. The reduction is necessary to achieve the desired accuracy. Also, the sequential nature of the PTO flow is inherently non-scalable. To address these limitations, we are proposing a quasi-hierarchical solution that combines multiple levels of parallelism to increase the scalability of the entire PTO flow. In this paper, we describe the system and present experimental results demonstrating the runtime reduction through scalable processing with thousands of computational cores.

Parallel Multivariate Spatio-Temporal Clustering of Large Ecological Datasets on Hybrid Supercomputers

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sreepathi, Sarat; Kumar, Jitendra; Mills, Richard T.

A proliferation of data from vast networks of remote sensing platforms (satellites, unmanned aircraft systems (UAS), airborne etc.), observational facilities (meteorological, eddy covariance etc.), state-of-the-art sensors, and simulation models offer unprecedented opportunities for scientific discovery. Unsupervised classification is a widely applied data mining approach to derive insights from such data. However, classification of very large data sets is a complex computational problem that requires efficient numerical algorithms and implementations on high performance computing (HPC) platforms. Additionally, increasing power, space, cooling and efficiency requirements has led to the deployment of hybrid supercomputing platforms with complex architectures and memory hierarchies like themore » Titan system at Oak Ridge National Laboratory. The advent of such accelerated computing architectures offers new challenges and opportunities for big data analytics in general and specifically, large scale cluster analysis in our case. Although there is an existing body of work on parallel cluster analysis, those approaches do not fully meet the needs imposed by the nature and size of our large data sets. Moreover, they had scaling limitations and were mostly limited to traditional distributed memory computing platforms. We present a parallel Multivariate Spatio-Temporal Clustering (MSTC) technique based on k-means cluster analysis that can target hybrid supercomputers like Titan. We developed a hybrid MPI, CUDA and OpenACC implementation that can utilize both CPU and GPU resources on computational nodes. We describe performance results on Titan that demonstrate the scalability and efficacy of our approach in processing large ecological data sets.« less

Scalable alignment and transfer of nanowires based on oriented polymer nanofibers.

PubMed

Yan, Shancheng; Lu, Lanxin; Meng, Hao; Huang, Ningping; Xiao, Zhongdang

2010-03-05

We develop a simple and scalable method based on oriented polymer nanofiber films for the parallel assembly and transfer of nanowires at high density. Nanowires dispersed in solution are aligned and selectively deposited at the central space of parallel nanochannels formed by the well-oriented nanofibers as a result of evaporation-induced flow and capillarity. A general contact printing method is used to realize the transfer of the nanowires from the donor nanofiber film to a receiver substrate. The mechanism, which involves ordered alignment of nanowires on oriented polymer nanofiber films, is also explored with an evaporation model of cylindrical droplets. The simplicity of the assembly and transfer, and the facile fabrication of large-area well-oriented nanofiber films, make the present method promising for the application of nanowires, especially for the disordered nanowires synthesized by solution chemistry.

FPGA-based prototype storage system with phase change memory

NASA Astrophysics Data System (ADS)

Li, Gezi; Chen, Xiaogang; Chen, Bomy; Li, Shunfen; Zhou, Mi; Han, Wenbing; Song, Zhitang

2016-10-01

With the ever-increasing amount of data being stored via social media, mobile telephony base stations, and network devices etc. the database systems face severe bandwidth bottlenecks when moving vast amounts of data from storage to the processing nodes. At the same time, Storage Class Memory (SCM) technologies such as Phase Change Memory (PCM) with unique features like fast read access, high density, non-volatility, byte-addressability, positive response to increasing temperature, superior scalability, and zero standby leakage have changed the landscape of modern computing and storage systems. In such a scenario, we present a storage system called FLEET which can off-load partial or whole SQL queries to the storage engine from CPU. FLEET uses an FPGA rather than conventional CPUs to implement the off-load engine due to its highly parallel nature. We have implemented an initial prototype of FLEET with PCM-based storage. The results demonstrate that significant performance and CPU utilization gains can be achieved by pushing selected query processing components inside in PCM-based storage.

Optical wireless communication in data centers

NASA Astrophysics Data System (ADS)

Arnon, Shlomi

2018-01-01

In the last decade data centers have become a crucial element in modern human society. However, to keep pace with internet data rate growth, new technologies supporting data center should develop. Integration of optical wireless communication (OWC) in data centers is one of the proposed technologies as augmented technology to the fiber network. One implementation of the OWC technology is deployment of optical wireless transceiver on top of the existing cable/fiber network as extension to the top of rack (TOR) switch; in this way, a dynamic and flexible network is created. Optical wireless communication could reduce energy consumption, increase the data rate, reduce the communication latency, increase flexibility and scalability, and reduce maintenance time and cost, in comparison to extra fiber network deployment. In this paper we review up to date literature in the field, propose an implementation scheme of OWC network, discuss ways to reduce energy consumption by parallel link communication and report preliminary measurement result of university data center environment.

Efficient Process Migration for Parallel Processing on Non-Dedicated Networks of Workstations

NASA Technical Reports Server (NTRS)

Chanchio, Kasidit; Sun, Xian-He

1996-01-01

This paper presents the design and preliminary implementation of MpPVM, a software system that supports process migration for PVM application programs in a non-dedicated heterogeneous computing environment. New concepts of migration point as well as migration point analysis and necessary data analysis are introduced. In MpPVM, process migrations occur only at previously inserted migration points. Migration point analysis determines appropriate locations to insert migration points; whereas, necessary data analysis provides a minimum set of variables to be transferred at each migration pint. A new methodology to perform reliable point-to-point data communications in a migration environment is also discussed. Finally, a preliminary implementation of MpPVM and its experimental results are presented, showing the correctness and promising performance of our process migration mechanism in a scalable non-dedicated heterogeneous computing environment. While MpPVM is developed on top of PVM, the process migration methodology introduced in this study is general and can be applied to any distributed software environment.

A Scalable Data Access Layer to Manage Structured Heterogeneous Biomedical Data.

PubMed

Delussu, Giovanni; Lianas, Luca; Frexia, Francesca; Zanetti, Gianluigi

2016-01-01

This work presents a scalable data access layer, called PyEHR, designed to support the implementation of data management systems for secondary use of structured heterogeneous biomedical and clinical data. PyEHR adopts the openEHR's formalisms to guarantee the decoupling of data descriptions from implementation details and exploits structure indexing to accelerate searches. Data persistence is guaranteed by a driver layer with a common driver interface. Interfaces for two NoSQL Database Management Systems are already implemented: MongoDB and Elasticsearch. We evaluated the scalability of PyEHR experimentally through two types of tests, called "Constant Load" and "Constant Number of Records", with queries of increasing complexity on synthetic datasets of ten million records each, containing very complex openEHR archetype structures, distributed on up to ten computing nodes.

Demonstration of Hadoop-GIS: A Spatial Data Warehousing System Over MapReduce.

PubMed

Aji, Ablimit; Sun, Xiling; Vo, Hoang; Liu, Qioaling; Lee, Rubao; Zhang, Xiaodong; Saltz, Joel; Wang, Fusheng

2013-11-01

The proliferation of GPS-enabled devices, and the rapid improvement of scientific instruments have resulted in massive amounts of spatial data in the last decade. Support of high performance spatial queries on large volumes data has become increasingly important in numerous fields, which requires a scalable and efficient spatial data warehousing solution as existing approaches exhibit scalability limitations and efficiency bottlenecks for large scale spatial applications. In this demonstration, we present Hadoop-GIS - a scalable and high performance spatial query system over MapReduce. Hadoop-GIS provides an efficient spatial query engine to process spatial queries, data and space based partitioning, and query pipelines that parallelize queries implicitly on MapReduce. Hadoop-GIS also provides an expressive, SQL-like spatial query language for workload specification. We will demonstrate how spatial queries are expressed in spatially extended SQL queries, and submitted through a command line/web interface for execution. Parallel to our system demonstration, we explain the system architecture and details on how queries are translated to MapReduce operators, optimized, and executed on Hadoop. In addition, we will showcase how the system can be used to support two representative real world use cases: large scale pathology analytical imaging, and geo-spatial data warehousing.

Exploiting multi-scale parallelism for large scale numerical modelling of laser wakefield accelerators

NASA Astrophysics Data System (ADS)

Fonseca, R. A.; Vieira, J.; Fiuza, F.; Davidson, A.; Tsung, F. S.; Mori, W. B.; Silva, L. O.

2013-12-01

A new generation of laser wakefield accelerators (LWFA), supported by the extreme accelerating fields generated in the interaction of PW-Class lasers and underdense targets, promises the production of high quality electron beams in short distances for multiple applications. Achieving this goal will rely heavily on numerical modelling to further understand the underlying physics and identify optimal regimes, but large scale modelling of these scenarios is computationally heavy and requires the efficient use of state-of-the-art petascale supercomputing systems. We discuss the main difficulties involved in running these simulations and the new developments implemented in the OSIRIS framework to address these issues, ranging from multi-dimensional dynamic load balancing and hybrid distributed/shared memory parallelism to the vectorization of the PIC algorithm. We present the results of the OASCR Joule Metric program on the issue of large scale modelling of LWFA, demonstrating speedups of over 1 order of magnitude on the same hardware. Finally, scalability to over ˜106 cores and sustained performance over ˜2 P Flops is demonstrated, opening the way for large scale modelling of LWFA scenarios.

LDRD project final report : hybrid AI/cognitive tactical behavior framework for LVC.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Djordjevich, Donna D.; Xavier, Patrick Gordon; Brannon, Nathan Gregory

This Lab-Directed Research and Development (LDRD) sought to develop technology that enhances scenario construction speed, entity behavior robustness, and scalability in Live-Virtual-Constructive (LVC) simulation. We investigated issues in both simulation architecture and behavior modeling. We developed path-planning technology that improves the ability to express intent in the planning task while still permitting an efficient search algorithm. An LVC simulation demonstrated how this enables 'one-click' layout of squad tactical paths, as well as dynamic re-planning for simulated squads and for real and simulated mobile robots. We identified human response latencies that can be exploited in parallel/distributed architectures. We did an experimentalmore » study to determine where parallelization would be productive in Umbra-based force-on-force (FOF) simulations. We developed and implemented a data-driven simulation composition approach that solves entity class hierarchy issues and supports assurance of simulation fairness. Finally, we proposed a flexible framework to enable integration of multiple behavior modeling components that model working memory phenomena with different degrees of sophistication.« less

Homemade Buckeye-Pi: A Learning Many-Node Platform for High-Performance Parallel Computing

NASA Astrophysics Data System (ADS)

Amooie, M. A.; Moortgat, J.

2017-12-01

We report on the "Buckeye-Pi" cluster, the supercomputer developed in The Ohio State University School of Earth Sciences from 128 inexpensive Raspberry Pi (RPi) 3 Model B single-board computers. Each RPi is equipped with fast Quad Core 1.2GHz ARMv8 64bit processor, 1GB of RAM, and 32GB microSD card for local storage. Therefore, the cluster has a total RAM of 128GB that is distributed on the individual nodes and a flash capacity of 4TB with 512 processors, while it benefits from low power consumption, easy portability, and low total cost. The cluster uses the Message Passing Interface protocol to manage the communications between each node. These features render our platform the most powerful RPi supercomputer to date and suitable for educational applications in high-performance-computing (HPC) and handling of large datasets. In particular, we use the Buckeye-Pi to implement optimized parallel codes in our in-house simulator for subsurface media flows with the goal of achieving a massively-parallelized scalable code. We present benchmarking results for the computational performance across various number of RPi nodes. We believe our project could inspire scientists and students to consider the proposed unconventional cluster architecture as a mainstream and a feasible learning platform for challenging engineering and scientific problems.

Comparison of temporal and spectral scattering methods using acoustically large breast models derived from magnetic resonance images.

PubMed

Hesford, Andrew J; Tillett, Jason C; Astheimer, Jeffrey P; Waag, Robert C

2014-08-01

Accurate and efficient modeling of ultrasound propagation through realistic tissue models is important to many aspects of clinical ultrasound imaging. Simplified problems with known solutions are often used to study and validate numerical methods. Greater confidence in a time-domain k-space method and a frequency-domain fast multipole method is established in this paper by analyzing results for realistic models of the human breast. Models of breast tissue were produced by segmenting magnetic resonance images of ex vivo specimens into seven distinct tissue types. After confirming with histologic analysis by pathologists that the model structures mimicked in vivo breast, the tissue types were mapped to variations in sound speed and acoustic absorption. Calculations of acoustic scattering by the resulting model were performed on massively parallel supercomputer clusters using parallel implementations of the k-space method and the fast multipole method. The efficient use of these resources was confirmed by parallel efficiency and scalability studies using large-scale, realistic tissue models. Comparisons between the temporal and spectral results were performed in representative planes by Fourier transforming the temporal results. An RMS field error less than 3% throughout the model volume confirms the accuracy of the methods for modeling ultrasound propagation through human breast.

Efficient computation of k-Nearest Neighbour Graphs for large high-dimensional data sets on GPU clusters.

PubMed

Dashti, Ali; Komarov, Ivan; D'Souza, Roshan M

2013-01-01

This paper presents an implementation of the brute-force exact k-Nearest Neighbor Graph (k-NNG) construction for ultra-large high-dimensional data cloud. The proposed method uses Graphics Processing Units (GPUs) and is scalable with multi-levels of parallelism (between nodes of a cluster, between different GPUs on a single node, and within a GPU). The method is applicable to homogeneous computing clusters with a varying number of nodes and GPUs per node. We achieve a 6-fold speedup in data processing as compared with an optimized method running on a cluster of CPUs and bring a hitherto impossible [Formula: see text]-NNG generation for a dataset of twenty million images with 15 k dimensionality into the realm of practical possibility.

Parallel Algorithms for the Exascale Era

DOE Office of Scientific and Technical Information (OSTI.GOV)

Robey, Robert W.

New parallel algorithms are needed to reach the Exascale level of parallelism with millions of cores. We look at some of the research developed by students in projects at LANL. The research blends ideas from the early days of computing while weaving in the fresh approach brought by students new to the field of high performance computing. We look at reproducibility of global sums and why it is important to parallel computing. Next we look at how the concept of hashing has led to the development of more scalable algorithms suitable for next-generation parallel computers. Nearly all of this workmore » has been done by undergraduates and published in leading scientific journals.« less

Argonne Simulation Framework for Intelligent Transportation Systems

DOT National Transportation Integrated Search

1996-01-01

A simulation framework has been developed which defines a high-level architecture for a large-scale, comprehensive, scalable simulation of an Intelligent Transportation System (ITS). The simulator is designed to run on parallel computers and distribu...

Scalable Quantum Networks for Distributed Computing and Sensing

DTIC Science & Technology

2016-04-01

probabilistic measurement , so we developed quantum memories and guided-wave implementations of same, demonstrating controlled delay of a heralded single...Second, fundamental scalability requires a method to synchronize protocols based on quantum measurements , which are inherently probabilistic. To meet...AFRL-AFOSR-UK-TR-2016-0007 Scalable Quantum Networks for Distributed Computing and Sensing Ian Walmsley THE UNIVERSITY OF OXFORD Final Report 04/01

Optimized distributed systems achieve significant performance improvement on sorted merging of massive VCF files.

PubMed

Sun, Xiaobo; Gao, Jingjing; Jin, Peng; Eng, Celeste; Burchard, Esteban G; Beaty, Terri H; Ruczinski, Ingo; Mathias, Rasika A; Barnes, Kathleen; Wang, Fusheng; Qin, Zhaohui S

2018-06-01

Sorted merging of genomic data is a common data operation necessary in many sequencing-based studies. It involves sorting and merging genomic data from different subjects by their genomic locations. In particular, merging a large number of variant call format (VCF) files is frequently required in large-scale whole-genome sequencing or whole-exome sequencing projects. Traditional single-machine based methods become increasingly inefficient when processing large numbers of files due to the excessive computation time and Input/Output bottleneck. Distributed systems and more recent cloud-based systems offer an attractive solution. However, carefully designed and optimized workflow patterns and execution plans (schemas) are required to take full advantage of the increased computing power while overcoming bottlenecks to achieve high performance. In this study, we custom-design optimized schemas for three Apache big data platforms, Hadoop (MapReduce), HBase, and Spark, to perform sorted merging of a large number of VCF files. These schemas all adopt the divide-and-conquer strategy to split the merging job into sequential phases/stages consisting of subtasks that are conquered in an ordered, parallel, and bottleneck-free way. In two illustrating examples, we test the performance of our schemas on merging multiple VCF files into either a single TPED or a single VCF file, which are benchmarked with the traditional single/parallel multiway-merge methods, message passing interface (MPI)-based high-performance computing (HPC) implementation, and the popular VCFTools. Our experiments suggest all three schemas either deliver a significant improvement in efficiency or render much better strong and weak scalabilities over traditional methods. Our findings provide generalized scalable schemas for performing sorted merging on genetics and genomics data using these Apache distributed systems.

Parallel Task Management Library for MARTe

NASA Astrophysics Data System (ADS)

Valcarcel, Daniel F.; Alves, Diogo; Neto, Andre; Reux, Cedric; Carvalho, Bernardo B.; Felton, Robert; Lomas, Peter J.; Sousa, Jorge; Zabeo, Luca

2014-06-01

The Multithreaded Application Real-Time executor (MARTe) is a real-time framework with increasing popularity and support in the thermonuclear fusion community. It allows modular code to run in a multi-threaded environment leveraging on the current multi-core processor (CPU) technology. One application that relies on the MARTe framework is the Joint European Torus (JET) tokamak WAll Load Limiter System (WALLS). It calculates and monitors the temperature on metal tiles and plasma facing components (PFCs) that can melt or flake if their temperature gets too high when exposed to power loads. One of the main time consuming tasks in WALLS is the calculation of thermal diffusion models in real-time. These models tend to be described by very large state-space models thus making them perfect candidates for parallelisation. MARTe's traditional approach for task parallelisation is to split the problem into several Real-Time Threads, each responsible for a self-contained sequential execution of an input-to-output chain. This is usually possible, but it might not always be practical for algorithmic or technical reasons. Also, it might not be easily scalable with an increase in the number of available CPU cores. The WorkLibrary introduces a “GPU-like approach” of splitting work among the available cores of modern CPUs that is (i) straightforward to use in an application, (ii) scalable with the availability of cores and all of this (iii) without rewriting or recompiling the source code. The first part of this article explains the motivation behind the library, its architecture and implementation. The second part presents a real application for WALLS, a parallel version of a large state-space model describing the 2D thermal diffusion on a JET tile.

Development of a Grid-Independent Geos-Chem Chemical Transport Model (v9-02) as an Atmospheric Chemistry Module for Earth System Models

NASA Technical Reports Server (NTRS)

Long, M. S.; Yantosca, R.; Nielsen, J. E; Keller, C. A.; Da Silva, A.; Sulprizio, M. P.; Pawson, S.; Jacob, D. J.

2015-01-01

The GEOS-Chem global chemical transport model (CTM), used by a large atmospheric chemistry research community, has been re-engineered to also serve as an atmospheric chemistry module for Earth system models (ESMs). This was done using an Earth System Modeling Framework (ESMF) interface that operates independently of the GEOSChem scientific code, permitting the exact same GEOSChem code to be used as an ESM module or as a standalone CTM. In this manner, the continual stream of updates contributed by the CTM user community is automatically passed on to the ESM module, which remains state of science and referenced to the latest version of the standard GEOS-Chem CTM. A major step in this re-engineering was to make GEOS-Chem grid independent, i.e., capable of using any geophysical grid specified at run time. GEOS-Chem data sockets were also created for communication between modules and with external ESM code. The grid-independent, ESMF-compatible GEOS-Chem is now the standard version of the GEOS-Chem CTM. It has been implemented as an atmospheric chemistry module into the NASA GEOS- 5 ESM. The coupled GEOS-5-GEOS-Chem system was tested for scalability and performance with a tropospheric oxidant-aerosol simulation (120 coupled species, 66 transported tracers) using 48-240 cores and message-passing interface (MPI) distributed-memory parallelization. Numerical experiments demonstrate that the GEOS-Chem chemistry module scales efficiently for the number of cores tested, with no degradation as the number of cores increases. Although inclusion of atmospheric chemistry in ESMs is computationally expensive, the excellent scalability of the chemistry module means that the relative cost goes down with increasing number of cores in a massively parallel environment.

PAGANI Toolkit: Parallel graph-theoretical analysis package for brain network big data.

PubMed

Du, Haixiao; Xia, Mingrui; Zhao, Kang; Liao, Xuhong; Yang, Huazhong; Wang, Yu; He, Yong

2018-05-01

The recent collection of unprecedented quantities of neuroimaging data with high spatial resolution has led to brain network big data. However, a toolkit for fast and scalable computational solutions is still lacking. Here, we developed the PArallel Graph-theoretical ANalysIs (PAGANI) Toolkit based on a hybrid central processing unit-graphics processing unit (CPU-GPU) framework with a graphical user interface to facilitate the mapping and characterization of high-resolution brain networks. Specifically, the toolkit provides flexible parameters for users to customize computations of graph metrics in brain network analyses. As an empirical example, the PAGANI Toolkit was applied to individual voxel-based brain networks with ∼200,000 nodes that were derived from a resting-state fMRI dataset of 624 healthy young adults from the Human Connectome Project. Using a personal computer, this toolbox completed all computations in ∼27 h for one subject, which is markedly less than the 118 h required with a single-thread implementation. The voxel-based functional brain networks exhibited prominent small-world characteristics and densely connected hubs, which were mainly located in the medial and lateral fronto-parietal cortices. Moreover, the female group had significantly higher modularity and nodal betweenness centrality mainly in the medial/lateral fronto-parietal and occipital cortices than the male group. Significant correlations between the intelligence quotient and nodal metrics were also observed in several frontal regions. Collectively, the PAGANI Toolkit shows high computational performance and good scalability for analyzing connectome big data and provides a friendly interface without the complicated configuration of computing environments, thereby facilitating high-resolution connectomics research in health and disease. © 2018 Wiley Periodicals, Inc.

Optimized distributed systems achieve significant performance improvement on sorted merging of massive VCF files

PubMed Central

Gao, Jingjing; Jin, Peng; Eng, Celeste; Burchard, Esteban G; Beaty, Terri H; Ruczinski, Ingo; Mathias, Rasika A; Barnes, Kathleen; Wang, Fusheng

2018-01-01

Abstract Background Sorted merging of genomic data is a common data operation necessary in many sequencing-based studies. It involves sorting and merging genomic data from different subjects by their genomic locations. In particular, merging a large number of variant call format (VCF) files is frequently required in large-scale whole-genome sequencing or whole-exome sequencing projects. Traditional single-machine based methods become increasingly inefficient when processing large numbers of files due to the excessive computation time and Input/Output bottleneck. Distributed systems and more recent cloud-based systems offer an attractive solution. However, carefully designed and optimized workflow patterns and execution plans (schemas) are required to take full advantage of the increased computing power while overcoming bottlenecks to achieve high performance. Findings In this study, we custom-design optimized schemas for three Apache big data platforms, Hadoop (MapReduce), HBase, and Spark, to perform sorted merging of a large number of VCF files. These schemas all adopt the divide-and-conquer strategy to split the merging job into sequential phases/stages consisting of subtasks that are conquered in an ordered, parallel, and bottleneck-free way. In two illustrating examples, we test the performance of our schemas on merging multiple VCF files into either a single TPED or a single VCF file, which are benchmarked with the traditional single/parallel multiway-merge methods, message passing interface (MPI)–based high-performance computing (HPC) implementation, and the popular VCFTools. Conclusions Our experiments suggest all three schemas either deliver a significant improvement in efficiency or render much better strong and weak scalabilities over traditional methods. Our findings provide generalized scalable schemas for performing sorted merging on genetics and genomics data using these Apache distributed systems. PMID:29762754

geoKepler Workflow Module for Computationally Scalable and Reproducible Geoprocessing and Modeling

NASA Astrophysics Data System (ADS)

Cowart, C.; Block, J.; Crawl, D.; Graham, J.; Gupta, A.; Nguyen, M.; de Callafon, R.; Smarr, L.; Altintas, I.

2015-12-01

The NSF-funded WIFIRE project has developed an open-source, online geospatial workflow platform for unifying geoprocessing tools and models for for fire and other geospatially dependent modeling applications. It is a product of WIFIRE's objective to build an end-to-end cyberinfrastructure for real-time and data-driven simulation, prediction and visualization of wildfire behavior. geoKepler includes a set of reusable GIS components, or actors, for the Kepler Scientific Workflow System (https://kepler-project.org). Actors exist for reading and writing GIS data in formats such as Shapefile, GeoJSON, KML, and using OGC web services such as WFS. The actors also allow for calling geoprocessing tools in other packages such as GDAL and GRASS. Kepler integrates functions from multiple platforms and file formats into one framework, thus enabling optimal GIS interoperability, model coupling, and scalability. Products of the GIS actors can be fed directly to models such as FARSITE and WRF. Kepler's ability to schedule and scale processes using Hadoop and Spark also makes geoprocessing ultimately extensible and computationally scalable. The reusable workflows in geoKepler can be made to run automatically when alerted by real-time environmental conditions. Here, we show breakthroughs in the speed of creating complex data for hazard assessments with this platform. We also demonstrate geoKepler workflows that use Data Assimilation to ingest real-time weather data into wildfire simulations, and for data mining techniques to gain insight into environmental conditions affecting fire behavior. Existing machine learning tools and libraries such as R and MLlib are being leveraged for this purpose in Kepler, as well as Kepler's Distributed Data Parallel (DDP) capability to provide a framework for scalable processing. geoKepler workflows can be executed via an iPython notebook as a part of a Jupyter hub at UC San Diego for sharing and reporting of the scientific analysis and results from various runs of geoKepler workflows. The communication between iPython and Kepler workflow executions is established through an iPython magic function for Kepler that we have implemented. In summary, geoKepler is an ecosystem that makes geospatial processing and analysis of any kind programmable, reusable, scalable and sharable.

Smart photodetector arrays for error control in page-oriented optical memory

NASA Astrophysics Data System (ADS)

Schaffer, Maureen Elizabeth

1998-12-01

Page-oriented optical memories (POMs) have been proposed to meet high speed, high capacity storage requirements for input/output intensive computer applications. This technology offers the capability for storage and retrieval of optical data in two-dimensional pages resulting in high throughput data rates. Since currently measured raw bit error rates for these systems fall several orders of magnitude short of industry requirements for binary data storage, powerful error control codes must be adopted. These codes must be designed to take advantage of the two-dimensional memory output. In addition, POMs require an optoelectronic interface to transfer the optical data pages to one or more electronic host systems. Conventional charge coupled device (CCD) arrays can receive optical data in parallel, but the relatively slow serial electronic output of these devices creates a system bottleneck thereby eliminating the POM advantage of high transfer rates. Also, CCD arrays are "unintelligent" interfaces in that they offer little data processing capabilities. The optical data page can be received by two-dimensional arrays of "smart" photo-detector elements that replace conventional CCD arrays. These smart photodetector arrays (SPAs) can perform fast parallel data decoding and error control, thereby providing an efficient optoelectronic interface between the memory and the electronic computer. This approach optimizes the computer memory system by combining the massive parallelism and high speed of optics with the diverse functionality, low cost, and local interconnection efficiency of electronics. In this dissertation we examine the design of smart photodetector arrays for use as the optoelectronic interface for page-oriented optical memory. We review options and technologies for SPA fabrication, develop SPA requirements, and determine SPA scalability constraints with respect to pixel complexity, electrical power dissipation, and optical power limits. Next, we examine data modulation and error correction coding for the purpose of error control in the POM system. These techniques are adapted, where possible, for 2D data and evaluated as to their suitability for a SPA implementation in terms of BER, code rate, decoder time and pixel complexity. Our analysis shows that differential data modulation combined with relatively simple block codes known as array codes provide a powerful means to achieve the desired data transfer rates while reducing error rates to industry requirements. Finally, we demonstrate the first smart photodetector array designed to perform parallel error correction on an entire page of data and satisfy the sustained data rates of page-oriented optical memories. Our implementation integrates a monolithic PN photodiode array and differential input receiver for optoelectronic signal conversion with a cluster error correction code using 0.35-mum CMOS. This approach provides high sensitivity, low electrical power dissipation, and fast parallel correction of 2 x 2-bit cluster errors in an 8 x 8 bit code block to achieve corrected output data rates scalable to 102 Gbps in the current technology increasing to 1.88 Tbps in 0.1-mum CMOS.

Speeding up parallel processing

NASA Technical Reports Server (NTRS)

Denning, Peter J.

1988-01-01

In 1967 Amdahl expressed doubts about the ultimate utility of multiprocessors. The formulation, now called Amdahl's law, became part of the computing folklore and has inspired much skepticism about the ability of the current generation of massively parallel processors to efficiently deliver all their computing power to programs. The widely publicized recent results of a group at Sandia National Laboratory, which showed speedup on a 1024 node hypercube of over 500 for three fixed size problems and over 1000 for three scalable problems, have convincingly challenged this bit of folklore and have given new impetus to parallel scientific computing.

Parallelizing Data-Centric Programs

DTIC Science & Technology

2013-09-25

results than current techniques, such as ImageWebs [HGO+10], given the same budget of matches performed. 4.2 Scalable Parallel Similarity Search The work...algorithms. 5 Data-Driven Applications in the Cloud In this project, we investigated what happens when data-centric software is moved from expensive custom ...returns appropriate answer tuples. Figure 9 (b) shows the mutual constraint satisfaction that takes place in answering for 122. The intent is that

Evaluation of the Intel Xeon Phi Co-processor to accelerate the sensitivity map calculation for PET imaging

NASA Astrophysics Data System (ADS)

Dey, T.; Rodrigue, P.

2015-07-01

We aim to evaluate the Intel Xeon Phi coprocessor for acceleration of 3D Positron Emission Tomography (PET) image reconstruction. We focus on the sensitivity map calculation as one computational intensive part of PET image reconstruction, since it is a promising candidate for acceleration with the Many Integrated Core (MIC) architecture of the Xeon Phi. The computation of the voxels in the field of view (FoV) can be done in parallel and the 103 to 104 samples needed to calculate the detection probability of each voxel can take advantage of vectorization. We use the ray tracing kernels of the Embree project to calculate the hit points of the sample rays with the detector and in a second step the sum of the radiological path taking into account attenuation is determined. The core components are implemented using the Intel single instruction multiple data compiler (ISPC) to enable a portable implementation showing efficient vectorization either on the Xeon Phi and the Host platform. On the Xeon Phi, the calculation of the radiological path is also implemented in hardware specific intrinsic instructions (so-called `intrinsics') to allow manually-optimized vectorization. For parallelization either OpenMP and ISPC tasking (based on pthreads) are evaluated.Our implementation achieved a scalability factor of 0.90 on the Xeon Phi coprocessor (model 5110P) with 60 cores at 1 GHz. Only minor differences were found between parallelization with OpenMP and the ISPC tasking feature. The implementation using intrinsics was found to be about 12% faster than the portable ISPC version. With this version, a speedup of 1.43 was achieved on the Xeon Phi coprocessor compared to the host system (HP SL250s Gen8) equipped with two Xeon (E5-2670) CPUs, with 8 cores at 2.6 to 3.3 GHz each. Using a second Xeon Phi card the speedup could be further increased to 2.77. No significant differences were found between the results of the different Xeon Phi and the Host implementations. The examination showed that a reasonable speedup of sensitivity map calculation could be achieved on the Xeon Phi either by a portable or a hardware specific implementation.

Real-time computing platform for spiking neurons (RT-spike).

PubMed

Ros, Eduardo; Ortigosa, Eva M; Agís, Rodrigo; Carrillo, Richard; Arnold, Michael

2006-07-01

A computing platform is described for simulating arbitrary networks of spiking neurons in real time. A hybrid computing scheme is adopted that uses both software and hardware components to manage the tradeoff between flexibility and computational power; the neuron model is implemented in hardware and the network model and the learning are implemented in software. The incremental transition of the software components into hardware is supported. We focus on a spike response model (SRM) for a neuron where the synapses are modeled as input-driven conductances. The temporal dynamics of the synaptic integration process are modeled with a synaptic time constant that results in a gradual injection of charge. This type of model is computationally expensive and is not easily amenable to existing software-based event-driven approaches. As an alternative we have designed an efficient time-based computing architecture in hardware, where the different stages of the neuron model are processed in parallel. Further improvements occur by computing multiple neurons in parallel using multiple processing units. This design is tested using reconfigurable hardware and its scalability and performance evaluated. Our overall goal is to investigate biologically realistic models for the real-time control of robots operating within closed action-perception loops, and so we evaluate the performance of the system on simulating a model of the cerebellum where the emulation of the temporal dynamics of the synaptic integration process is important.

MPI-FAUN: An MPI-Based Framework for Alternating-Updating Nonnegative Matrix Factorization

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kannan, Ramakrishnan; Ballard, Grey; Park, Haesun

Non-negative matrix factorization (NMF) is the problem of determining two non-negative low rank factors W and H, for the given input matrix A, such that A≈WH. NMF is a useful tool for many applications in different domains such as topic modeling in text mining, background separation in video analysis, and community detection in social networks. Despite its popularity in the data mining community, there is a lack of efficient parallel algorithms to solve the problem for big data sets. The main contribution of this work is a new, high-performance parallel computational framework for a broad class of NMF algorithms thatmore » iteratively solves alternating non-negative least squares (NLS) subproblems for W and H. It maintains the data and factor matrices in memory (distributed across processors), uses MPI for interprocessor communication, and, in the dense case, provably minimizes communication costs (under mild assumptions). The framework is flexible and able to leverage a variety of NMF and NLS algorithms, including Multiplicative Update, Hierarchical Alternating Least Squares, and Block Principal Pivoting. Our implementation allows us to benchmark and compare different algorithms on massive dense and sparse data matrices of size that spans from few hundreds of millions to billions. We demonstrate the scalability of our algorithm and compare it with baseline implementations, showing significant performance improvements. The code and the datasets used for conducting the experiments are available online.« less

MPI-FAUN: An MPI-Based Framework for Alternating-Updating Nonnegative Matrix Factorization

DOE PAGES

Kannan, Ramakrishnan; Ballard, Grey; Park, Haesun

2017-10-30

Non-negative matrix factorization (NMF) is the problem of determining two non-negative low rank factors W and H, for the given input matrix A, such that A≈WH. NMF is a useful tool for many applications in different domains such as topic modeling in text mining, background separation in video analysis, and community detection in social networks. Despite its popularity in the data mining community, there is a lack of efficient parallel algorithms to solve the problem for big data sets. The main contribution of this work is a new, high-performance parallel computational framework for a broad class of NMF algorithms thatmore » iteratively solves alternating non-negative least squares (NLS) subproblems for W and H. It maintains the data and factor matrices in memory (distributed across processors), uses MPI for interprocessor communication, and, in the dense case, provably minimizes communication costs (under mild assumptions). The framework is flexible and able to leverage a variety of NMF and NLS algorithms, including Multiplicative Update, Hierarchical Alternating Least Squares, and Block Principal Pivoting. Our implementation allows us to benchmark and compare different algorithms on massive dense and sparse data matrices of size that spans from few hundreds of millions to billions. We demonstrate the scalability of our algorithm and compare it with baseline implementations, showing significant performance improvements. The code and the datasets used for conducting the experiments are available online.« less

A Scalable Data Access Layer to Manage Structured Heterogeneous Biomedical Data

PubMed Central

Lianas, Luca; Frexia, Francesca; Zanetti, Gianluigi

2016-01-01

This work presents a scalable data access layer, called PyEHR, designed to support the implementation of data management systems for secondary use of structured heterogeneous biomedical and clinical data. PyEHR adopts the openEHR’s formalisms to guarantee the decoupling of data descriptions from implementation details and exploits structure indexing to accelerate searches. Data persistence is guaranteed by a driver layer with a common driver interface. Interfaces for two NoSQL Database Management Systems are already implemented: MongoDB and Elasticsearch. We evaluated the scalability of PyEHR experimentally through two types of tests, called “Constant Load” and “Constant Number of Records”, with queries of increasing complexity on synthetic datasets of ten million records each, containing very complex openEHR archetype structures, distributed on up to ten computing nodes. PMID:27936191

ls1 mardyn: The Massively Parallel Molecular Dynamics Code for Large Systems.

PubMed

Niethammer, Christoph; Becker, Stefan; Bernreuther, Martin; Buchholz, Martin; Eckhardt, Wolfgang; Heinecke, Alexander; Werth, Stephan; Bungartz, Hans-Joachim; Glass, Colin W; Hasse, Hans; Vrabec, Jadran; Horsch, Martin

2014-10-14

The molecular dynamics simulation code ls1 mardyn is presented. It is a highly scalable code, optimized for massively parallel execution on supercomputing architectures and currently holds the world record for the largest molecular simulation with over four trillion particles. It enables the application of pair potentials to length and time scales that were previously out of scope for molecular dynamics simulation. With an efficient dynamic load balancing scheme, it delivers high scalability even for challenging heterogeneous configurations. Presently, multicenter rigid potential models based on Lennard-Jones sites, point charges, and higher-order polarities are supported. Due to its modular design, ls1 mardyn can be extended to new physical models, methods, and algorithms, allowing future users to tailor it to suit their respective needs. Possible applications include scenarios with complex geometries, such as fluids at interfaces, as well as nonequilibrium molecular dynamics simulation of heat and mass transfer.

Utilizing High-Performance Computing to Investigate Parameter Sensitivity of an Inversion Model for Vadose Zone Flow and Transport

NASA Astrophysics Data System (ADS)

Fang, Z.; Ward, A. L.; Fang, Y.; Yabusaki, S.

2011-12-01

High-resolution geologic models have proven effective in improving the accuracy of subsurface flow and transport predictions. However, many of the parameters in subsurface flow and transport models cannot be determined directly at the scale of interest and must be estimated through inverse modeling. A major challenge, particularly in vadose zone flow and transport, is the inversion of the highly-nonlinear, high-dimensional problem as current methods are not readily scalable for large-scale, multi-process models. In this paper we describe the implementation of a fully automated approach for addressing complex parameter optimization and sensitivity issues on massively parallel multi- and many-core systems. The approach is based on the integration of PNNL's extreme scale Subsurface Transport Over Multiple Phases (eSTOMP) simulator, which uses the Global Array toolkit, with the Beowulf-Cluster inspired parallel nonlinear parameter estimation software, BeoPEST in the MPI mode. In the eSTOMP/BeoPEST implementation, a pre-processor generates all of the PEST input files based on the eSTOMP input file. Simulation results for comparison with observations are extracted automatically at each time step eliminating the need for post-process data extractions. The inversion framework was tested with three different experimental data sets: one-dimensional water flow at Hanford Grass Site; irrigation and infiltration experiment at the Andelfingen Site; and a three-dimensional injection experiment at Hanford's Sisson and Lu Site. Good agreements are achieved in all three applications between observations and simulations in both parameter estimates and water dynamics reproduction. Results show that eSTOMP/BeoPEST approach is highly scalable and can be run efficiently with hundreds or thousands of processors. BeoPEST is fault tolerant and new nodes can be dynamically added and removed. A major advantage of this approach is the ability to use high-resolution geologic models to preserve the spatial structure in the inverse model, which leads to better parameter estimates and improved predictions when using the inverse-conditioned realizations of parameter fields.

High-rate serial interconnections for embedded and distributed systems with power and resource constraints

NASA Astrophysics Data System (ADS)

Sheynin, Yuriy; Shutenko, Felix; Suvorova, Elena; Yablokov, Evgenej

2008-04-01

High rate interconnections are important subsystems in modern data processing and control systems of many classes. They are especially important in prospective embedded and on-board systems that used to be multicomponent systems with parallel or distributed architecture, [1]. Modular architecture systems of previous generations were based on parallel busses that were widely used and standardised: VME, PCI, CompactPCI, etc. Busses evolution went in improvement of bus protocol efficiency (burst transactions, split transactions, etc.) and increasing operation frequencies. However, due to multi-drop bus nature and multi-wire skew problems the parallel bussing speedup became more and more limited. For embedded and on-board systems additional reason for this trend was in weight, size and power constraints of an interconnection and its components. Parallel interfaces have become technologically more challenging as their respective clock frequencies have increased to keep pace with the bandwidth requirements of their attached storage devices. Since each interface uses a data clock to gate and validate the parallel data (which is normally 8 bits or 16 bits wide), the clock frequency need only be equivalent to the byte rate or word rate being transmitted. In other words, for a given transmission frequency, the wider the data bus, the slower the clock. As the clock frequency increases, more high frequency energy is available in each of the data lines, and a portion of this energy is dissipated in radiation. Each data line not only transmits this energy but also receives some from its neighbours. This form of mutual interference is commonly called "cross-talk," and the signal distortion it produces can become another major contributor to loss of data integrity unless compensated by appropriate cable designs. Other transmission problems such as frequency-dependent attenuation and signal reflections, while also applicable to serial interfaces, are more troublesome in parallel interfaces due to the number of additional cable conductors involved. In order to compensate for these drawbacks, higher quality cables, shorter cable runs and fewer devices on the bus have been the norm. Finally, the physical bulk of the parallel cables makes them more difficult to route inside an enclosure, hinders cooling airflow and is incompatible with the trend toward smaller form-factor devices. Parallel busses worked in systems during the past 20 years, but the accumulated problems dictate the need for change and the technology is available to spur the transition. The general trend in high-rate interconnections turned from parallel bussing to scalable interconnections with a network architecture and high-rate point-to-point links. Analysis showed that data links with serial information transfer could achieve higher throughput and efficiency and it was confirmed in various research and practical design. Serial interfaces offer an improvement over older parallel interfaces: better performance, better scalability, and also better reliability as the parallel interfaces are at their limits of speed with reliable data transfers and others. The trend was implemented in major standards' families evolution: e.g. from PCI/PCI-X parallel bussing to PCIExpress interconnection architecture with serial lines, from CompactPCI parallel bus to ATCA (Advanced Telecommunications Architecture) specification with serial links and network topologies of an interconnection, etc. In the article we consider a general set of characteristics and features of serial interconnections, give a brief overview of serial interconnections specifications. In more details we present the SpaceWire interconnection technology. Have been developed for space on-board systems applications the SpaceWire has important features and characteristics that make it a prospective interconnection for wide range of embedded systems.

Accurate and Efficient Parallel Implementation of an Effective Linear-Scaling Direct Random Phase Approximation Method.

PubMed

Graf, Daniel; Beuerle, Matthias; Schurkus, Henry F; Luenser, Arne; Savasci, Gökcen; Ochsenfeld, Christian

2018-05-08

An efficient algorithm for calculating the random phase approximation (RPA) correlation energy is presented that is as accurate as the canonical molecular orbital resolution-of-the-identity RPA (RI-RPA) with the important advantage of an effective linear-scaling behavior (instead of quartic) for large systems due to a formulation in the local atomic orbital space. The high accuracy is achieved by utilizing optimized minimax integration schemes and the local Coulomb metric attenuated by the complementary error function for the RI approximation. The memory bottleneck of former atomic orbital (AO)-RI-RPA implementations ( Schurkus, H. F.; Ochsenfeld, C. J. Chem. Phys. 2016 , 144 , 031101 and Luenser, A.; Schurkus, H. F.; Ochsenfeld, C. J. Chem. Theory Comput. 2017 , 13 , 1647 - 1655 ) is addressed by precontraction of the large 3-center integral matrix with the Cholesky factors of the ground state density reducing the memory requirements of that matrix by a factor of [Formula: see text]. Furthermore, we present a parallel implementation of our method, which not only leads to faster RPA correlation energy calculations but also to a scalable decrease in memory requirements, opening the door for investigations of large molecules even on small- to medium-sized computing clusters. Although it is known that AO methods are highly efficient for extended systems, where sparsity allows for reaching the linear-scaling regime, we show that our work also extends the applicability when considering highly delocalized systems for which no linear scaling can be achieved. As an example, the interlayer distance of two covalent organic framework pore fragments (comprising 384 atoms in total) is analyzed.

A New Conflict Resolution Method for Multiple Mobile Robots in Cluttered Environments With Motion-Liveness.

PubMed

Shahriari, Mohammadali; Biglarbegian, Mohammad

2018-01-01

This paper presents a new conflict resolution methodology for multiple mobile robots while ensuring their motion-liveness, especially for cluttered and dynamic environments. Our method constructs a mathematical formulation in a form of an optimization problem by minimizing the overall travel times of the robots subject to resolving all the conflicts in their motion. This optimization problem can be easily solved through coordinating only the robots' speeds. To overcome the computational cost in executing the algorithm for very cluttered environments, we develop an innovative method through clustering the environment into independent subproblems that can be solved using parallel programming techniques. We demonstrate the scalability of our approach through performing extensive simulations. Simulation results showed that our proposed method is capable of resolving the conflicts of 100 robots in less than 1.23 s in a cluttered environment that has 4357 intersections in the paths of the robots. We also developed an experimental testbed and demonstrated that our approach can be implemented in real time. We finally compared our approach with other existing methods in the literature both quantitatively and qualitatively. This comparison shows while our approach is mathematically sound, it is more computationally efficient, scalable for very large number of robots, and guarantees the live and smooth motion of robots.

A scalable PC-based parallel computer for lattice QCD

NASA Astrophysics Data System (ADS)

Fodor, Z.; Katz, S. D.; Pappa, G.

2003-05-01

A PC-based parallel computer for medium/large scale lattice QCD simulations is suggested. The Eo¨tvo¨s Univ., Inst. Theor. Phys. cluster consists of 137 Intel P4-1.7GHz nodes. Gigabit Ethernet cards are used for nearest neighbor communication in a two-dimensional mesh. The sustained performance for dynamical staggered (wilson) quarks on large lattices is around 70(110) GFlops. The exceptional price/performance ratio is below $1/Mflop.

Scalable Metadata Management for a Large Multi-Source Seismic Data Repository

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gaylord, J. M.; Dodge, D. A.; Magana-Zook, S. A.

In this work, we implemented the key metadata management components of a scalable seismic data ingestion framework to address limitations in our existing system, and to position it for anticipated growth in volume and complexity.

Performance analysis of distributed symmetric sparse matrix vector multiplication algorithm for multi-core architectures

DOE PAGES

Oryspayev, Dossay; Aktulga, Hasan Metin; Sosonkina, Masha; ...

2015-07-14

In this article, sparse matrix vector multiply (SpMVM) is an important kernel that frequently arises in high performance computing applications. Due to its low arithmetic intensity, several approaches have been proposed in literature to improve its scalability and efficiency in large scale computations. In this paper, our target systems are high end multi-core architectures and we use messaging passing interface + open multiprocessing hybrid programming model for parallelism. We analyze the performance of recently proposed implementation of the distributed symmetric SpMVM, originally developed for large sparse symmetric matrices arising in ab initio nuclear structure calculations. We also study important featuresmore » of this implementation and compare with previously reported implementations that do not exploit underlying symmetry. Our SpMVM implementations leverage the hybrid paradigm to efficiently overlap expensive communications with computations. Our main comparison criterion is the "CPU core hours" metric, which is the main measure of resource usage on supercomputers. We analyze the effects of topology-aware mapping heuristic using simplified network load model. Furthermore, we have tested the different SpMVM implementations on two large clusters with 3D Torus and Dragonfly topology. Our results show that the distributed SpMVM implementation that exploits matrix symmetry and hides communication yields the best value for the "CPU core hours" metric and significantly reduces data movement overheads.« less

Compiled MPI: Cost-Effective Exascale Applications Development

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bronevetsky, G; Quinlan, D; Lumsdaine, A

2012-04-10

The complexity of petascale and exascale machines makes it increasingly difficult to develop applications that can take advantage of them. Future systems are expected to feature billion-way parallelism, complex heterogeneous compute nodes and poor availability of memory (Peter Kogge, 2008). This new challenge for application development is motivating a significant amount of research and development on new programming models and runtime systems designed to simplify large-scale application development. Unfortunately, DoE has significant multi-decadal investment in a large family of mission-critical scientific applications. Scaling these applications to exascale machines will require a significant investment that will dwarf the costs of hardwaremore » procurement. A key reason for the difficulty in transitioning today's applications to exascale hardware is their reliance on explicit programming techniques, such as the Message Passing Interface (MPI) programming model to enable parallelism. MPI provides a portable and high performance message-passing system that enables scalable performance on a wide variety of platforms. However, it also forces developers to lock the details of parallelization together with application logic, making it very difficult to adapt the application to significant changes in the underlying system. Further, MPI's explicit interface makes it difficult to separate the application's synchronization and communication structure, reducing the amount of support that can be provided by compiler and run-time tools. This is in contrast to the recent research on more implicit parallel programming models such as Chapel, OpenMP and OpenCL, which promise to provide significantly more flexibility at the cost of reimplementing significant portions of the application. We are developing CoMPI, a novel compiler-driven approach to enable existing MPI applications to scale to exascale systems with minimal modifications that can be made incrementally over the application's lifetime. It includes: (1) New set of source code annotations, inserted either manually or automatically, that will clarify the application's use of MPI to the compiler infrastructure, enabling greater accuracy where needed; (2) A compiler transformation framework that leverages these annotations to transform the original MPI source code to improve its performance and scalability; (3) Novel MPI runtime implementation techniques that will provide a rich set of functionality extensions to be used by applications that have been transformed by our compiler; and (4) A novel compiler analysis that leverages simple user annotations to automatically extract the application's communication structure and synthesize most complex code annotations.« less

Architectural Considerations for Highly Scalable Computing to Support On-demand Video Analytics

DTIC Science & Technology

2017-04-19

enforcement . The system was tested in the wild using video files as well as a commercial Video Management System supporting more than 100 surveillance...research were used to implement a distributed on-demand video analytics system that was prototyped for the use of forensics investigators in law...cameras as video sources. The architectural considerations of this system are presented. Issues to be reckoned with in implementing a scalable

Implementation of Complex Biological Logic Circuits Using Spatially Distributed Multicellular Consortia

PubMed Central

Urrios, Arturo; de Nadal, Eulàlia; Solé, Ricard; Posas, Francesc

2016-01-01

Engineered synthetic biological devices have been designed to perform a variety of functions from sensing molecules and bioremediation to energy production and biomedicine. Notwithstanding, a major limitation of in vivo circuit implementation is the constraint associated to the use of standard methodologies for circuit design. Thus, future success of these devices depends on obtaining circuits with scalable complexity and reusable parts. Here we show how to build complex computational devices using multicellular consortia and space as key computational elements. This spatial modular design grants scalability since its general architecture is independent of the circuit’s complexity, minimizes wiring requirements and allows component reusability with minimal genetic engineering. The potential use of this approach is demonstrated by implementation of complex logical functions with up to six inputs, thus demonstrating the scalability and flexibility of this method. The potential implications of our results are outlined. PMID:26829588

Performance-scalable volumetric data classification for online industrial inspection

NASA Astrophysics Data System (ADS)

Abraham, Aby J.; Sadki, Mustapha; Lea, R. M.

2002-03-01

Non-intrusive inspection and non-destructive testing of manufactured objects with complex internal structures typically requires the enhancement, analysis and visualization of high-resolution volumetric data. Given the increasing availability of fast 3D scanning technology (e.g. cone-beam CT), enabling on-line detection and accurate discrimination of components or sub-structures, the inherent complexity of classification algorithms inevitably leads to throughput bottlenecks. Indeed, whereas typical inspection throughput requirements range from 1 to 1000 volumes per hour, depending on density and resolution, current computational capability is one to two orders-of-magnitude less. Accordingly, speeding up classification algorithms requires both reduction of algorithm complexity and acceleration of computer performance. A shape-based classification algorithm, offering algorithm complexity reduction, by using ellipses as generic descriptors of solids-of-revolution, and supporting performance-scalability, by exploiting the inherent parallelism of volumetric data, is presented. A two-stage variant of the classical Hough transform is used for ellipse detection and correlation of the detected ellipses facilitates position-, scale- and orientation-invariant component classification. Performance-scalability is achieved cost-effectively by accelerating a PC host with one or more COTS (Commercial-Off-The-Shelf) PCI multiprocessor cards. Experimental results are reported to demonstrate the feasibility and cost-effectiveness of the data-parallel classification algorithm for on-line industrial inspection applications.

Introducing a distributed unstructured mesh into gyrokinetic particle-in-cell code, XGC

NASA Astrophysics Data System (ADS)

Yoon, Eisung; Shephard, Mark; Seol, E. Seegyoung; Kalyanaraman, Kaushik

2017-10-01

XGC has shown good scalability for large leadership supercomputers. The current production version uses a copy of the entire unstructured finite element mesh on every MPI rank. Although an obvious scalability issue if the mesh sizes are to be dramatically increased, the current approach is also not optimal with respect to data locality of particles and mesh information. To address these issues we have initiated the development of a distributed mesh PIC method. This approach directly addresses the base scalability issue with respect to mesh size and, through the use of a mesh entity centric view of the particle mesh relationship, provides opportunities to address data locality needs of many core and GPU supported heterogeneous systems. The parallel mesh PIC capabilities are being built on the Parallel Unstructured Mesh Infrastructure (PUMI). The presentation will first overview the form of mesh distribution used and indicate the structures and functions used to support the mesh, the particles and their interaction. Attention will then focus on the node-level optimizations being carried out to ensure performant operation of all PIC operations on the distributed mesh. Partnership for Edge Physics Simulation (EPSI) Grant No. DE-SC0008449 and Center for Extended Magnetohydrodynamic Modeling (CEMM) Grant No. DE-SC0006618.

Demonstration of Hadoop-GIS: A Spatial Data Warehousing System Over MapReduce

PubMed Central

Aji, Ablimit; Sun, Xiling; Vo, Hoang; Liu, Qioaling; Lee, Rubao; Zhang, Xiaodong; Saltz, Joel; Wang, Fusheng

2016-01-01

The proliferation of GPS-enabled devices, and the rapid improvement of scientific instruments have resulted in massive amounts of spatial data in the last decade. Support of high performance spatial queries on large volumes data has become increasingly important in numerous fields, which requires a scalable and efficient spatial data warehousing solution as existing approaches exhibit scalability limitations and efficiency bottlenecks for large scale spatial applications. In this demonstration, we present Hadoop-GIS – a scalable and high performance spatial query system over MapReduce. Hadoop-GIS provides an efficient spatial query engine to process spatial queries, data and space based partitioning, and query pipelines that parallelize queries implicitly on MapReduce. Hadoop-GIS also provides an expressive, SQL-like spatial query language for workload specification. We will demonstrate how spatial queries are expressed in spatially extended SQL queries, and submitted through a command line/web interface for execution. Parallel to our system demonstration, we explain the system architecture and details on how queries are translated to MapReduce operators, optimized, and executed on Hadoop. In addition, we will showcase how the system can be used to support two representative real world use cases: large scale pathology analytical imaging, and geo-spatial data warehousing. PMID:27617325

A Hybrid Task Graph Scheduler for High Performance Image Processing Workflows.

PubMed

Blattner, Timothy; Keyrouz, Walid; Bhattacharyya, Shuvra S; Halem, Milton; Brady, Mary

2017-12-01

Designing applications for scalability is key to improving their performance in hybrid and cluster computing. Scheduling code to utilize parallelism is difficult, particularly when dealing with data dependencies, memory management, data motion, and processor occupancy. The Hybrid Task Graph Scheduler (HTGS) improves programmer productivity when implementing hybrid workflows for multi-core and multi-GPU systems. The Hybrid Task Graph Scheduler (HTGS) is an abstract execution model, framework, and API that increases programmer productivity when implementing hybrid workflows for such systems. HTGS manages dependencies between tasks, represents CPU and GPU memories independently, overlaps computations with disk I/O and memory transfers, keeps multiple GPUs occupied, and uses all available compute resources. Through these abstractions, data motion and memory are explicit; this makes data locality decisions more accessible. To demonstrate the HTGS application program interface (API), we present implementations of two example algorithms: (1) a matrix multiplication that shows how easily task graphs can be used; and (2) a hybrid implementation of microscopy image stitching that reduces code size by ≈ 43% compared to a manually coded hybrid workflow implementation and showcases the minimal overhead of task graphs in HTGS. Both of the HTGS-based implementations show good performance. In image stitching the HTGS implementation achieves similar performance to the hybrid workflow implementation. Matrix multiplication with HTGS achieves 1.3× and 1.8× speedup over the multi-threaded OpenBLAS library for 16k × 16k and 32k × 32k size matrices, respectively.

Tensorial Basis Spline Collocation Method for Poisson's Equation

NASA Astrophysics Data System (ADS)

Plagne, Laurent; Berthou, Jean-Yves

2000-01-01

This paper aims to describe the tensorial basis spline collocation method applied to Poisson's equation. In the case of a localized 3D charge distribution in vacuum, this direct method based on a tensorial decomposition of the differential operator is shown to be competitive with both iterative BSCM and FFT-based methods. We emphasize the O(h4) and O(h6) convergence of TBSCM for cubic and quintic splines, respectively. We describe the implementation of this method on a distributed memory parallel machine. Performance measurements on a Cray T3E are reported. Our code exhibits high performance and good scalability: As an example, a 27 Gflops performance is obtained when solving Poisson's equation on a 2563 non-uniform 3D Cartesian mesh by using 128 T3E-750 processors. This represents 215 Mflops per processors.

Using NCAR Yellowstone for PhotoVoltaic Power Forecasts with Artificial Neural Networks and an Analog Ensemble

NASA Astrophysics Data System (ADS)

Cervone, G.; Clemente-Harding, L.; Alessandrini, S.; Delle Monache, L.

2016-12-01

A methodology based on Artificial Neural Networks (ANN) and an Analog Ensemble (AnEn) is presented to generate 72-hour deterministic and probabilistic forecasts of power generated by photovoltaic (PV) power plants using input from a numerical weather prediction model and computed astronomical variables. ANN and AnEn are used individually and in combination to generate forecasts for three solar power plant located in Italy. The computational scalability of the proposed solution is tested using synthetic data simulating 4,450 PV power stations. The NCAR Yellowstone supercomputer is employed to test the parallel implementation of the proposed solution, ranging from 1 node (32 cores) to 4,450 nodes (141,140 cores). Results show that a combined AnEn + ANN solution yields best results, and that the proposed solution is well suited for massive scale computation.

Distributed Fast Self-Organized Maps for Massive Spectrophotometric Data Analysis †.

PubMed

Dafonte, Carlos; Garabato, Daniel; Álvarez, Marco A; Manteiga, Minia

2018-05-03

Analyzing huge amounts of data becomes essential in the era of Big Data, where databases are populated with hundreds of Gigabytes that must be processed to extract knowledge. Hence, classical algorithms must be adapted towards distributed computing methodologies that leverage the underlying computational power of these platforms. Here, a parallel, scalable, and optimized design for self-organized maps (SOM) is proposed in order to analyze massive data gathered by the spectrophotometric sensor of the European Space Agency (ESA) Gaia spacecraft, although it could be extrapolated to other domains. The performance comparison between the sequential implementation and the distributed ones based on Apache Hadoop and Apache Spark is an important part of the work, as well as the detailed analysis of the proposed optimizations. Finally, a domain-specific visualization tool to explore astronomical SOMs is presented.

Scalable Performance Environments for Parallel Systems

NASA Technical Reports Server (NTRS)

Reed, Daniel A.; Olson, Robert D.; Aydt, Ruth A.; Madhyastha, Tara M.; Birkett, Thomas; Jensen, David W.; Nazief, Bobby A. A.; Totty, Brian K.

1991-01-01

As parallel systems expand in size and complexity, the absence of performance tools for these parallel systems exacerbates the already difficult problems of application program and system software performance tuning. Moreover, given the pace of technological change, we can no longer afford to develop ad hoc, one-of-a-kind performance instrumentation software; we need scalable, portable performance analysis tools. We describe an environment prototype based on the lessons learned from two previous generations of performance data analysis software. Our environment prototype contains a set of performance data transformation modules that can be interconnected in user-specified ways. It is the responsibility of the environment infrastructure to hide details of module interconnection and data sharing. The environment is written in C++ with the graphical displays based on X windows and the Motif toolkit. It allows users to interconnect and configure modules graphically to form an acyclic, directed data analysis graph. Performance trace data are represented in a self-documenting stream format that includes internal definitions of data types, sizes, and names. The environment prototype supports the use of head-mounted displays and sonic data presentation in addition to the traditional use of visual techniques.

Theoretical and Empirical Analysis of a Spatial EA Parallel Boosting Algorithm.

PubMed

Kamath, Uday; Domeniconi, Carlotta; De Jong, Kenneth

2018-01-01

Many real-world problems involve massive amounts of data. Under these circumstances learning algorithms often become prohibitively expensive, making scalability a pressing issue to be addressed. A common approach is to perform sampling to reduce the size of the dataset and enable efficient learning. Alternatively, one customizes learning algorithms to achieve scalability. In either case, the key challenge is to obtain algorithmic efficiency without compromising the quality of the results. In this article we discuss a meta-learning algorithm (PSBML) that combines concepts from spatially structured evolutionary algorithms (SSEAs) with concepts from ensemble and boosting methodologies to achieve the desired scalability property. We present both theoretical and empirical analyses which show that PSBML preserves a critical property of boosting, specifically, convergence to a distribution centered around the margin. We then present additional empirical analyses showing that this meta-level algorithm provides a general and effective framework that can be used in combination with a variety of learning classifiers. We perform extensive experiments to investigate the trade-off achieved between scalability and accuracy, and robustness to noise, on both synthetic and real-world data. These empirical results corroborate our theoretical analysis, and demonstrate the potential of PSBML in achieving scalability without sacrificing accuracy.

Parallel rendering

NASA Technical Reports Server (NTRS)

Crockett, Thomas W.

1995-01-01

This article provides a broad introduction to the subject of parallel rendering, encompassing both hardware and software systems. The focus is on the underlying concepts and the issues which arise in the design of parallel rendering algorithms and systems. We examine the different types of parallelism and how they can be applied in rendering applications. Concepts from parallel computing, such as data decomposition, task granularity, scalability, and load balancing, are considered in relation to the rendering problem. We also explore concepts from computer graphics, such as coherence and projection, which have a significant impact on the structure of parallel rendering algorithms. Our survey covers a number of practical considerations as well, including the choice of architectural platform, communication and memory requirements, and the problem of image assembly and display. We illustrate the discussion with numerous examples from the parallel rendering literature, representing most of the principal rendering methods currently used in computer graphics.

A Scalable Architecture of a Structured LDPC Decoder

NASA Technical Reports Server (NTRS)

Lee, Jason Kwok-San; Lee, Benjamin; Thorpe, Jeremy; Andrews, Kenneth; Dolinar, Sam; Hamkins, Jon

2004-01-01

We present a scalable decoding architecture for a certain class of structured LDPC codes. The codes are designed using a small (n,r) protograph that is replicated Z times to produce a decoding graph for a (Z x n, Z x r) code. Using this architecture, we have implemented a decoder for a (4096,2048) LDPC code on a Xilinx Virtex-II 2000 FPGA, and achieved decoding speeds of 31 Mbps with 10 fixed iterations. The implemented message-passing algorithm uses an optimized 3-bit non-uniform quantizer that operates with 0.2dB implementation loss relative to a floating point decoder.

A scalable variational inequality approach for flow through porous media models with pressure-dependent viscosity

NASA Astrophysics Data System (ADS)

Mapakshi, N. K.; Chang, J.; Nakshatrala, K. B.

2018-04-01

Mathematical models for flow through porous media typically enjoy the so-called maximum principles, which place bounds on the pressure field. It is highly desirable to preserve these bounds on the pressure field in predictive numerical simulations, that is, one needs to satisfy discrete maximum principles (DMP). Unfortunately, many of the existing formulations for flow through porous media models do not satisfy DMP. This paper presents a robust, scalable numerical formulation based on variational inequalities (VI), to model non-linear flows through heterogeneous, anisotropic porous media without violating DMP. VI is an optimization technique that places bounds on the numerical solutions of partial differential equations. To crystallize the ideas, a modification to Darcy equations by taking into account pressure-dependent viscosity will be discretized using the lowest-order Raviart-Thomas (RT0) and Variational Multi-scale (VMS) finite element formulations. It will be shown that these formulations violate DMP, and, in fact, these violations increase with an increase in anisotropy. It will be shown that the proposed VI-based formulation provides a viable route to enforce DMP. Moreover, it will be shown that the proposed formulation is scalable, and can work with any numerical discretization and weak form. A series of numerical benchmark problems are solved to demonstrate the effects of heterogeneity, anisotropy and non-linearity on DMP violations under the two chosen formulations (RT0 and VMS), and that of non-linearity on solver convergence for the proposed VI-based formulation. Parallel scalability on modern computational platforms will be illustrated through strong-scaling studies, which will prove the efficiency of the proposed formulation in a parallel setting. Algorithmic scalability as the problem size is scaled up will be demonstrated through novel static-scaling studies. The performed static-scaling studies can serve as a guide for users to be able to select an appropriate discretization for a given problem size.

Scalable asynchronous execution of cellular automata

NASA Astrophysics Data System (ADS)

Folino, Gianluigi; Giordano, Andrea; Mastroianni, Carlo

2016-10-01

The performance and scalability of cellular automata, when executed on parallel/distributed machines, are limited by the necessity of synchronizing all the nodes at each time step, i.e., a node can execute only after the execution of the previous step at all the other nodes. However, these synchronization requirements can be relaxed: a node can execute one step after synchronizing only with the adjacent nodes. In this fashion, different nodes can execute different time steps. This can be a notable advantageous in many novel and increasingly popular applications of cellular automata, such as smart city applications, simulation of natural phenomena, etc., in which the execution times can be different and variable, due to the heterogeneity of machines and/or data and/or executed functions. Indeed, a longer execution time at a node does not slow down the execution at all the other nodes but only at the neighboring nodes. This is particularly advantageous when the nodes that act as bottlenecks vary during the application execution. The goal of the paper is to analyze the benefits that can be achieved with the described asynchronous implementation of cellular automata, when compared to the classical all-to-all synchronization pattern. The performance and scalability have been evaluated through a Petri net model, as this model is very useful to represent the synchronization barrier among nodes. We examined the usual case in which the territory is partitioned into a number of regions, and the computation associated with a region is assigned to a computing node. We considered both the cases of mono-dimensional and two-dimensional partitioning. The results show that the advantage obtained through the asynchronous execution, when compared to the all-to-all synchronous approach is notable, and it can be as large as 90% in terms of speedup.

Casimir effect and graphene: Tunability, scalability, Casimir rotor

NASA Astrophysics Data System (ADS)

Martinez, J. C.; Chen, X.; Jalil, M. B. A.

2018-01-01

We study the combined effects of separated parallel disks, birefringence and surface currents on the Casimir force and torque. All three contribute to the Casimir force and surface currents from graphene permit tuning and switching from attraction to repulsion thus allowing for an oscillating Casimir force which can be relevant to parametric amplification applications. Only the latter two contribute to the Casimir torque and their combined effect can enhance the torque by at least tenfold (possibly more) compared to that due to birefringence alone, a hint at a scalable Casimir torque. We also consider a feasible non-contact rotor.

A Hierarchical and Distributed Approach for Mapping Large Applications to Heterogeneous Grids using Genetic Algorithms

NASA Technical Reports Server (NTRS)

Sanyal, Soumya; Jain, Amit; Das, Sajal K.; Biswas, Rupak

2003-01-01

In this paper, we propose a distributed approach for mapping a single large application to a heterogeneous grid environment. To minimize the execution time of the parallel application, we distribute the mapping overhead to the available nodes of the grid. This approach not only provides a fast mapping of tasks to resources but is also scalable. We adopt a hierarchical grid model and accomplish the job of mapping tasks to this topology using a scheduler tree. Results show that our three-phase algorithm provides high quality mappings, and is fast and scalable.

The P-Mesh: A Commodity-based Scalable Network Architecture for Clusters

NASA Technical Reports Server (NTRS)

Nitzberg, Bill; Kuszmaul, Chris; Stockdale, Ian; Becker, Jeff; Jiang, John; Wong, Parkson; Tweten, David (Technical Monitor)

1998-01-01

We designed a new network architecture, the P-Mesh which combines the scalability and fault resilience of a torus with the performance of a switch. We compare the scalability, performance, and cost of the hub, switch, torus, tree, and P-Mesh architectures. The latter three are capable of scaling to thousands of nodes, however, the torus has severe performance limitations with that many processors. The tree and P-Mesh have similar latency, bandwidth, and bisection bandwidth, but the P-Mesh outperforms the switch architecture (a lower bound for tree performance) on 16-node NAB Parallel Benchmark tests by up to 23%, and costs 40% less. Further, the P-Mesh has better fault resilience characteristics. The P-Mesh architecture trades increased management overhead for lower cost, and is a good bridging technology while the price of tree uplinks is expensive.

al3c: high-performance software for parameter inference using Approximate Bayesian Computation.

PubMed

Stram, Alexander H; Marjoram, Paul; Chen, Gary K

2015-11-01

The development of Approximate Bayesian Computation (ABC) algorithms for parameter inference which are both computationally efficient and scalable in parallel computing environments is an important area of research. Monte Carlo rejection sampling, a fundamental component of ABC algorithms, is trivial to distribute over multiple processors but is inherently inefficient. While development of algorithms such as ABC Sequential Monte Carlo (ABC-SMC) help address the inherent inefficiencies of rejection sampling, such approaches are not as easily scaled on multiple processors. As a result, current Bayesian inference software offerings that use ABC-SMC lack the ability to scale in parallel computing environments. We present al3c, a C++ framework for implementing ABC-SMC in parallel. By requiring only that users define essential functions such as the simulation model and prior distribution function, al3c abstracts the user from both the complexities of parallel programming and the details of the ABC-SMC algorithm. By using the al3c framework, the user is able to scale the ABC-SMC algorithm in parallel computing environments for his or her specific application, with minimal programming overhead. al3c is offered as a static binary for Linux and OS-X computing environments. The user completes an XML configuration file and C++ plug-in template for the specific application, which are used by al3c to obtain the desired results. Users can download the static binaries, source code, reference documentation and examples (including those in this article) by visiting https://github.com/ahstram/al3c. astram@usc.edu Supplementary data are available at Bioinformatics online. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Using Hadoop MapReduce for Parallel Genetic Algorithms: A Comparison of the Global, Grid and Island Models.

PubMed

Ferrucci, Filomena; Salza, Pasquale; Sarro, Federica

2017-06-29

The need to improve the scalability of Genetic Algorithms (GAs) has motivated the research on Parallel Genetic Algorithms (PGAs), and different technologies and approaches have been used. Hadoop MapReduce represents one of the most mature technologies to develop parallel algorithms. Based on the fact that parallel algorithms introduce communication overhead, the aim of the present work is to understand if, and possibly when, the parallel GAs solutions using Hadoop MapReduce show better performance than sequential versions in terms of execution time. Moreover, we are interested in understanding which PGA model can be most effective among the global, grid, and island models. We empirically assessed the performance of these three parallel models with respect to a sequential GA on a software engineering problem, evaluating the execution time and the achieved speedup. We also analysed the behaviour of the parallel models in relation to the overhead produced by the use of Hadoop MapReduce and the GAs' computational effort, which gives a more machine-independent measure of these algorithms. We exploited three problem instances to differentiate the computation load and three cluster configurations based on 2, 4, and 8 parallel nodes. Moreover, we estimated the costs of the execution of the experimentation on a potential cloud infrastructure, based on the pricing of the major commercial cloud providers. The empirical study revealed that the use of PGA based on the island model outperforms the other parallel models and the sequential GA for all the considered instances and clusters. Using 2, 4, and 8 nodes, the island model achieves an average speedup over the three datasets of 1.8, 3.4, and 7.0 times, respectively. Hadoop MapReduce has a set of different constraints that need to be considered during the design and the implementation of parallel algorithms. The overhead of data store (i.e., HDFS) accesses, communication, and latency requires solutions that reduce data store operations. For this reason, the island model is more suitable for PGAs than the global and grid model, also in terms of costs when executed on a commercial cloud provider.

Reconfigurable Hardware for Compressing Hyperspectral Image Data

NASA Technical Reports Server (NTRS)

Aranki, Nazeeh; Namkung, Jeffrey; Villapando, Carlos; Kiely, Aaron; Klimesh, Matthew; Xie, Hua

2010-01-01

High-speed, low-power, reconfigurable electronic hardware has been developed to implement ICER-3D, an algorithm for compressing hyperspectral-image data. The algorithm and parts thereof have been the topics of several NASA Tech Briefs articles, including Context Modeler for Wavelet Compression of Hyperspectral Images (NPO-43239) and ICER-3D Hyperspectral Image Compression Software (NPO-43238), which appear elsewhere in this issue of NASA Tech Briefs. As described in more detail in those articles, the algorithm includes three main subalgorithms: one for computing wavelet transforms, one for context modeling, and one for entropy encoding. For the purpose of designing the hardware, these subalgorithms are treated as modules to be implemented efficiently in field-programmable gate arrays (FPGAs). The design takes advantage of industry- standard, commercially available FPGAs. The implementation targets the Xilinx Virtex II pro architecture, which has embedded PowerPC processor cores with flexible on-chip bus architecture. It incorporates an efficient parallel and pipelined architecture to compress the three-dimensional image data. The design provides for internal buffering to minimize intensive input/output operations while making efficient use of offchip memory. The design is scalable in that the subalgorithms are implemented as independent hardware modules that can be combined in parallel to increase throughput. The on-chip processor manages the overall operation of the compression system, including execution of the top-level control functions as well as scheduling, initiating, and monitoring processes. The design prototype has been demonstrated to be capable of compressing hyperspectral data at a rate of 4.5 megasamples per second at a conservative clock frequency of 50 MHz, with a potential for substantially greater throughput at a higher clock frequency. The power consumption of the prototype is less than 6.5 W. The reconfigurability (by means of reprogramming) of the FPGAs makes it possible to effectively alter the design to some extent to satisfy different requirements without adding hardware. The implementation could be easily propagated to future FPGA generations and/or to custom application-specific integrated circuits.

Coordinated Transformation among Community Colleges Lacking a State System

ERIC Educational Resources Information Center

Russell, James Thad

2016-01-01

Community colleges face many challenges in the face of demands for increased student success. Institutions continually seek scalable interventions and initiatives focused on improving student achievement. Effectively implementing sustainable change that moves the needle of student success remains elusive. Facilitating systemic, scalable change…

Tuning collective communication for Partitioned Global Address Space programming models

DOE PAGES

Nishtala, Rajesh; Zheng, Yili; Hargrove, Paul H.; ...

2011-06-12

Partitioned Global Address Space (PGAS) languages offer programmers the convenience of a shared memory programming style combined with locality control necessary to run on large-scale distributed memory systems. Even within a PGAS language programmers often need to perform global communication operations such as broadcasts or reductions, which are best performed as collective operations in which a group of threads work together to perform the operation. In this study we consider the problem of implementing collective communication within PGAS languages and explore some of the design trade-offs in both the interface and implementation. In particular, PGAS collectives have semantic issues thatmore » are different than in send–receive style message passing programs, and different implementation approaches that take advantage of the one-sided communication style in these languages. We present an implementation framework for PGAS collectives as part of the GASNet communication layer, which supports shared memory, distributed memory and hybrids. The framework supports a broad set of algorithms for each collective, over which the implementation may be automatically tuned. In conclusion, we demonstrate the benefit of optimized GASNet collectives using application benchmarks written in UPC, and demonstrate that the GASNet collectives can deliver scalable performance on a variety of state-of-the-art parallel machines including a Cray XT4, an IBM BlueGene/P, and a Sun Constellation system with InfiniBand interconnect.« less

Increasing feasibility of the field-programmable gate array implementation of an iterative image registration using a kernel-warping algorithm

NASA Astrophysics Data System (ADS)

Nguyen, An Hung; Guillemette, Thomas; Lambert, Andrew J.; Pickering, Mark R.; Garratt, Matthew A.

2017-09-01

Image registration is a fundamental image processing technique. It is used to spatially align two or more images that have been captured at different times, from different sensors, or from different viewpoints. There have been many algorithms proposed for this task. The most common of these being the well-known Lucas-Kanade (LK) and Horn-Schunck approaches. However, the main limitation of these approaches is the computational complexity required to implement the large number of iterations necessary for successful alignment of the images. Previously, a multi-pass image interpolation algorithm (MP-I2A) was developed to considerably reduce the number of iterations required for successful registration compared with the LK algorithm. This paper develops a kernel-warping algorithm (KWA), a modified version of the MP-I2A, which requires fewer iterations to successfully register two images and less memory space for the field-programmable gate array (FPGA) implementation than the MP-I2A. These reductions increase feasibility of the implementation of the proposed algorithm on FPGAs with very limited memory space and other hardware resources. A two-FPGA system rather than single FPGA system is successfully developed to implement the KWA in order to compensate insufficiency of hardware resources supported by one FPGA, and increase parallel processing ability and scalability of the system.

Artificial dielectric polarizing-beamsplitter and isolator for the terahertz region.

PubMed

Mendis, Rajind; Nagai, Masaya; Zhang, Wei; Mittleman, Daniel M

2017-07-19

We demonstrate a simple and effective strategy for implementing a polarizing beamsplitter for the terahertz spectral region, based on an artificial dielectric medium that is scalable to a range of desired frequencies. The artificial dielectric medium consists of a uniformly spaced stack of metal plates, which is electromagnetically equivalent to a stacked array of parallel-plate waveguides. The operation of the device relies on both the lowest-order, transverse-electric and transverse-magnetic modes of the parallel-plate waveguide. This is in contrast to previous work that relied solely on the transverse-electric mode. The fabricated polarizing beamsplitter exhibits extinction ratios as high as 42 dB along with insertion losses as low as 0.18 dB. Building on the same idea, we also demonstrate an isolator with non-reciprocal transmission, providing high isolation and low insertion loss at a select design frequency. The performance of our isolator far exceeds that of other experimentally demonstrated terahertz isolators, and indeed, even rivals that of commercially available isolators for optical wavelengths. Because these waveguide-based artificial dielectrics are low loss, inexpensive, and easy to fabricate, this approach offers a promising new route for polarization control of free-space terahertz beams.

Simulation of 2D Kinetic Effects in Plasmas using the Grid Based Continuum Code LOKI

NASA Astrophysics Data System (ADS)

Banks, Jeffrey; Berger, Richard; Chapman, Tom; Brunner, Stephan

2016-10-01

Kinetic simulation of multi-dimensional plasma waves through direct discretization of the Vlasov equation is a useful tool to study many physical interactions and is particularly attractive for situations where minimal fluctuation levels are desired, for instance, when measuring growth rates of plasma wave instabilities. However, direct discretization of phase space can be computationally expensive, and as a result there are few examples of published results using Vlasov codes in more than a single configuration space dimension. In an effort to fill this gap we have developed the Eulerian-based kinetic code LOKI that evolves the Vlasov-Poisson system in 2+2-dimensional phase space. The code is designed to reduce the cost of phase-space computation by using fully 4th order accurate conservative finite differencing, while retaining excellent parallel scalability that efficiently uses large scale computing resources. In this poster I will discuss the algorithms used in the code as well as some aspects of their parallel implementation using MPI. I will also overview simulation results of basic plasma wave instabilities relevant to laser plasma interaction, which have been obtained using the code.

Earth system modelling on system-level heterogeneous architectures: EMAC (version 2.42) on the Dynamical Exascale Entry Platform (DEEP)

NASA Astrophysics Data System (ADS)

Christou, Michalis; Christoudias, Theodoros; Morillo, Julián; Alvarez, Damian; Merx, Hendrik

2016-09-01

We examine an alternative approach to heterogeneous cluster-computing in the many-core era for Earth system models, using the European Centre for Medium-Range Weather Forecasts Hamburg (ECHAM)/Modular Earth Submodel System (MESSy) Atmospheric Chemistry (EMAC) model as a pilot application on the Dynamical Exascale Entry Platform (DEEP). A set of autonomous coprocessors interconnected together, called Booster, complements a conventional HPC Cluster and increases its computing performance, offering extra flexibility to expose multiple levels of parallelism and achieve better scalability. The EMAC model atmospheric chemistry code (Module Efficiently Calculating the Chemistry of the Atmosphere (MECCA)) was taskified with an offload mechanism implemented using OmpSs directives. The model was ported to the MareNostrum 3 supercomputer to allow testing with Intel Xeon Phi accelerators on a production-size machine. The changes proposed in this paper are expected to contribute to the eventual adoption of Cluster-Booster division and Many Integrated Core (MIC) accelerated architectures in presently available implementations of Earth system models, towards exploiting the potential of a fully Exascale-capable platform.

Accelerating the Mining of Influential Nodes in Complex Networks through Community Detection

DOE Office of Scientific and Technical Information (OSTI.GOV)

Halappanavar, Mahantesh; Sathanur, Arun V.; Nandi, Apurba

Computing the set of influential nodes with a given size to ensure maximal spread of influence on a complex network is a challenging problem impacting multiple applications. A rigorous approach to influence maximization involves utilization of optimization routines that comes with a high computational cost. In this work, we propose to exploit the existence of communities in complex networks to accelerate the mining of influential seeds. We provide intuitive reasoning to explain why our approach should be able to provide speedups without significantly degrading the extent of the spread of influence when compared to the case of influence maximization withoutmore » using the community information. Additionally, we have parallelized the complete workflow by leveraging an existing parallel implementation of the Louvain community detection algorithm. We then conduct a series of experiments on a dataset with three representative graphs to first verify our implementation and then demonstrate the speedups. Our method achieves speedups ranging from 3x - 28x for graphs with small number of communities while nearly matching or even exceeding the activation performance on the entire graph. Complexity analysis reveals that dramatic speedups are possible for larger graphs that contain a correspondingly larger number of communities. In addition to the speedups obtained from the utilization of the community structure, scalability results show up to 6.3x speedup on 20 cores relative to the baseline run on 2 cores. Finally, current limitations of the approach are outlined along with the planned next steps.« less

Flexible parallel implicit modelling of coupled thermal-hydraulic-mechanical processes in fractured rocks

NASA Astrophysics Data System (ADS)

Cacace, Mauro; Jacquey, Antoine B.

2017-09-01

Theory and numerical implementation describing groundwater flow and the transport of heat and solute mass in fully saturated fractured rocks with elasto-plastic mechanical feedbacks are developed. In our formulation, fractures are considered as being of lower dimension than the hosting deformable porous rock and we consider their hydraulic and mechanical apertures as scaling parameters to ensure continuous exchange of fluid mass and energy within the fracture-solid matrix system. The coupled system of equations is implemented in a new simulator code that makes use of a Galerkin finite-element technique. The code builds on a flexible, object-oriented numerical framework (MOOSE, Multiphysics Object Oriented Simulation Environment) which provides an extensive scalable parallel and implicit coupling to solve for the multiphysics problem. The governing equations of groundwater flow, heat and mass transport, and rock deformation are solved in a weak sense (either by classical Newton-Raphson or by free Jacobian inexact Newton-Krylow schemes) on an underlying unstructured mesh. Nonlinear feedbacks among the active processes are enforced by considering evolving fluid and rock properties depending on the thermo-hydro-mechanical state of the system and the local structure, i.e. degree of connectivity, of the fracture system. A suite of applications is presented to illustrate the flexibility and capability of the new simulator to address problems of increasing complexity and occurring at different spatial (from centimetres to tens of kilometres) and temporal scales (from minutes to hundreds of years).

An Adaptable Seismic Data Format for Modern Scientific Workflows

NASA Astrophysics Data System (ADS)

Smith, J. A.; Bozdag, E.; Krischer, L.; Lefebvre, M.; Lei, W.; Podhorszki, N.; Tromp, J.

2013-12-01

Data storage, exchange, and access play a critical role in modern seismology. Current seismic data formats, such as SEED, SAC, and SEG-Y, were designed with specific applications in mind and are frequently a major bottleneck in implementing efficient workflows. We propose a new modern parallel format that can be adapted for a variety of seismic workflows. The Adaptable Seismic Data Format (ASDF) features high-performance parallel read and write support and the ability to store an arbitrary number of traces of varying sizes. Provenance information is stored inside the file so that users know the origin of the data as well as the precise operations that have been applied to the waveforms. The design of the new format is based on several real-world use cases, including earthquake seismology and seismic interferometry. The metadata is based on the proven XML schemas StationXML and QuakeML. Existing time-series analysis tool-kits are easily interfaced with this new format so that seismologists can use robust, previously developed software packages, such as ObsPy and the SAC library. ADIOS, netCDF4, and HDF5 can be used as the underlying container format. At Princeton University, we have chosen to use ADIOS as the container format because it has shown superior scalability for certain applications, such as dealing with big data on HPC systems. In the context of high-performance computing, we have implemented ASDF into the global adjoint tomography workflow on Oak Ridge National Laboratory's supercomputer Titan.

Spherical harmonic results for the 3D Kobayashi Benchmark suite

DOE Office of Scientific and Technical Information (OSTI.GOV)

Brown, P N; Chang, B; Hanebutte, U R

1999-03-02

Spherical harmonic solutions are presented for the Kobayashi benchmark suite. The results were obtained with Ardra, a scalable, parallel neutron transport code developed at Lawrence Livermore National Laboratory (LLNL). The calculations were performed on the IBM ASCI Blue-Pacific computer at LLNL.

Scalable Computing of the Mesh Size Effect on Modeling Damage Mechanics in Woven Armor Composites

DTIC Science & Technology

2008-12-01

manner of a user defined material subroutine to provide overall stress increments to, the parallel LS-DYNA3D a Lagrangian explicit code used in...finite element code, as a user defined material subroutine . The ability of this subroutine to model the effect of the progressions of a select number...is added as a user defined material subroutine to parallel LS-DYNA3D. The computations of the global mesh are handled by LS-DYNA3D and are spread

A Linked-Cell Domain Decomposition Method for Molecular Dynamics Simulation on a Scalable Multiprocessor

DOE PAGES

Yang, L. H.; Brooks III, E. D.; Belak, J.

1992-01-01

A molecular dynamics algorithm for performing large-scale simulations using the Parallel C Preprocessor (PCP) programming paradigm on the BBN TC2000, a massively parallel computer, is discussed. The algorithm uses a linked-cell data structure to obtain the near neighbors of each atom as time evoles. Each processor is assigned to a geometric domain containing many subcells and the storage for that domain is private to the processor. Within this scheme, the interdomain (i.e., interprocessor) communication is minimized.

HACC: Extreme Scaling and Performance Across Diverse Architectures

NASA Astrophysics Data System (ADS)

Habib, Salman; Morozov, Vitali; Frontiere, Nicholas; Finkel, Hal; Pope, Adrian; Heitmann, Katrin

2013-11-01

Supercomputing is evolving towards hybrid and accelerator-based architectures with millions of cores. The HACC (Hardware/Hybrid Accelerated Cosmology Code) framework exploits this diverse landscape at the largest scales of problem size, obtaining high scalability and sustained performance. Developed to satisfy the science requirements of cosmological surveys, HACC melds particle and grid methods using a novel algorithmic structure that flexibly maps across architectures, including CPU/GPU, multi/many-core, and Blue Gene systems. We demonstrate the success of HACC on two very different machines, the CPU/GPU system Titan and the BG/Q systems Sequoia and Mira, attaining unprecedented levels of scalable performance. We demonstrate strong and weak scaling on Titan, obtaining up to 99.2% parallel efficiency, evolving 1.1 trillion particles. On Sequoia, we reach 13.94 PFlops (69.2% of peak) and 90% parallel efficiency on 1,572,864 cores, with 3.6 trillion particles, the largest cosmological benchmark yet performed. HACC design concepts are applicable to several other supercomputer applications.

OWL: A scalable Monte Carlo simulation suite for finite-temperature study of materials

NASA Astrophysics Data System (ADS)

Li, Ying Wai; Yuk, Simuck F.; Cooper, Valentino R.; Eisenbach, Markus; Odbadrakh, Khorgolkhuu

The OWL suite is a simulation package for performing large-scale Monte Carlo simulations. Its object-oriented, modular design enables it to interface with various external packages for energy evaluations. It is therefore applicable to study the finite-temperature properties for a wide range of systems: from simple classical spin models to materials where the energy is evaluated by ab initio methods. This scheme not only allows for the study of thermodynamic properties based on first-principles statistical mechanics, it also provides a means for massive, multi-level parallelism to fully exploit the capacity of modern heterogeneous computer architectures. We will demonstrate how improved strong and weak scaling is achieved by employing novel, parallel and scalable Monte Carlo algorithms, as well as the applications of OWL to a few selected frontier materials research problems. This research was supported by the Office of Science of the Department of Energy under contract DE-AC05-00OR22725.

Final Report for Project DE-FC02-06ER25755 [Pmodels2

DOE Office of Scientific and Technical Information (OSTI.GOV)

Panda, Dhabaleswar; Sadayappan, P.

2014-03-12

In this report, we describe the research accomplished by the OSU team under the Pmodels2 project. The team has worked on various angles: designing high performance MPI implementations on modern networking technologies (Mellanox InfiniBand (including the new ConnectX2 architecture and Quad Data Rate), QLogic InfiniPath, the emerging 10GigE/iWARP and RDMA over Converged Enhanced Ethernet (RoCE) and Obsidian IB-WAN), studying MPI scalability issues for multi-thousand node clusters using XRC transport, scalable job start-up, dynamic process management support, efficient one-sided communication, protocol offloading and designing scalable collective communication libraries for emerging multi-core architectures. New designs conforming to the Argonne’s Nemesis interface havemore » also been carried out. All of these above solutions have been integrated into the open-source MVAPICH/MVAPICH2 software. This software is currently being used by more than 2,100 organizations worldwide (in 71 countries). As of January ’14, more than 200,000 downloads have taken place from the OSU Web site. In addition, many InfiniBand vendors, server vendors, system integrators and Linux distributors have been incorporating MVAPICH/MVAPICH2 into their software stacks and distributing it. Several InfiniBand systems using MVAPICH/MVAPICH2 have obtained positions in the TOP500 ranking of supercomputers in the world. The latest November ’13 ranking include the following systems: 7th ranked Stampede system at TACC with 462,462 cores; 11th ranked Tsubame 2.5 system at Tokyo Institute of Technology with 74,358 cores; 16th ranked Pleiades system at NASA with 81,920 cores; Work on PGAS models has proceeded on multiple directions. The Scioto framework, which supports task-parallelism in one-sided and global-view parallel programming, has been extended to allow multi-processor tasks that are executed by processor groups. A quantum Monte Carlo application is being ported onto the extended Scioto framework. A public release of Global Trees (GT) has been made, along with the Global Chunks (GC) framework on which GT is built. The Global Chunks (GC) layer is also being used as the basis for the development of a higher level Global Graphs (GG) layer. The Global Graphs (GG) system will provide a global address space view of distributed graph data structures on distributed memory systems.« less

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chen, Chao; Pouransari, Hadi; Rajamanickam, Sivasankaran

We present a parallel hierarchical solver for general sparse linear systems on distributed-memory machines. For large-scale problems, this fully algebraic algorithm is faster and more memory-efficient than sparse direct solvers because it exploits the low-rank structure of fill-in blocks. Depending on the accuracy of low-rank approximations, the hierarchical solver can be used either as a direct solver or as a preconditioner. The parallel algorithm is based on data decomposition and requires only local communication for updating boundary data on every processor. Moreover, the computation-to-communication ratio of the parallel algorithm is approximately the volume-to-surface-area ratio of the subdomain owned by everymore » processor. We also provide various numerical results to demonstrate the versatility and scalability of the parallel algorithm.« less

Highly Scalable Asynchronous Computing Method for Partial Differential Equations: A Path Towards Exascale

NASA Astrophysics Data System (ADS)

Konduri, Aditya

Many natural and engineering systems are governed by nonlinear partial differential equations (PDEs) which result in a multiscale phenomena, e.g. turbulent flows. Numerical simulations of these problems are computationally very expensive and demand for extreme levels of parallelism. At realistic conditions, simulations are being carried out on massively parallel computers with hundreds of thousands of processing elements (PEs). It has been observed that communication between PEs as well as their synchronization at these extreme scales take up a significant portion of the total simulation time and result in poor scalability of codes. This issue is likely to pose a bottleneck in scalability of codes on future Exascale systems. In this work, we propose an asynchronous computing algorithm based on widely used finite difference methods to solve PDEs in which synchronization between PEs due to communication is relaxed at a mathematical level. We show that while stability is conserved when schemes are used asynchronously, accuracy is greatly degraded. Since message arrivals at PEs are random processes, so is the behavior of the error. We propose a new statistical framework in which we show that average errors drop always to first-order regardless of the original scheme. We propose new asynchrony-tolerant schemes that maintain accuracy when synchronization is relaxed. The quality of the solution is shown to depend, not only on the physical phenomena and numerical schemes, but also on the characteristics of the computing machine. A novel algorithm using remote memory access communications has been developed to demonstrate excellent scalability of the method for large-scale computing. Finally, we present a path to extend this method in solving complex multi-scale problems on Exascale machines.

A Framework for Load Balancing of Tensor Contraction Expressions via Dynamic Task Partitioning

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lai, Pai-Wei; Stock, Kevin; Rajbhandari, Samyam

In this paper, we introduce the Dynamic Load-balanced Tensor Contractions (DLTC), a domain-specific library for efficient task parallel execution of tensor contraction expressions, a class of computation encountered in quantum chemistry and physics. Our framework decomposes each contraction into smaller unit of tasks, represented by an abstraction referred to as iterators. We exploit an extra level of parallelism by having tasks across independent contractions executed concurrently through a dynamic load balancing run- time. We demonstrate the improved performance, scalability, and flexibility for the computation of tensor contraction expressions on parallel computers using examples from coupled cluster methods.

PhreeqcRM: A reaction module for transport simulators based on the geochemical model PHREEQC

USGS Publications Warehouse

Parkhurst, David L.; Wissmeier, Laurin

2015-01-01

PhreeqcRM is a geochemical reaction module designed specifically to perform equilibrium and kinetic reaction calculations for reactive transport simulators that use an operator-splitting approach. The basic function of the reaction module is to take component concentrations from the model cells of the transport simulator, run geochemical reactions, and return updated component concentrations to the transport simulator. If multicomponent diffusion is modeled (e.g., Nernst–Planck equation), then aqueous species concentrations can be used instead of component concentrations. The reaction capabilities are a complete implementation of the reaction capabilities of PHREEQC. In each cell, the reaction module maintains the composition of all of the reactants, which may include minerals, exchangers, surface complexers, gas phases, solid solutions, and user-defined kinetic reactants.PhreeqcRM assigns initial and boundary conditions for model cells based on standard PHREEQC input definitions (files or strings) of chemical compositions of solutions and reactants. Additional PhreeqcRM capabilities include methods to eliminate reaction calculations for inactive parts of a model domain, transfer concentrations and other model properties, and retrieve selected results. The module demonstrates good scalability for parallel processing by using multiprocessing with MPI (message passing interface) on distributed memory systems, and limited scalability using multithreading with OpenMP on shared memory systems. PhreeqcRM is written in C++, but interfaces allow methods to be called from C or Fortran. By using the PhreeqcRM reaction module, an existing multicomponent transport simulator can be extended to simulate a wide range of geochemical reactions. Results of the implementation of PhreeqcRM as the reaction engine for transport simulators PHAST and FEFLOW are shown by using an analytical solution and the reactive transport benchmark of MoMaS.

A framework for plasticity implementation on the SpiNNaker neural architecture.

PubMed

Galluppi, Francesco; Lagorce, Xavier; Stromatias, Evangelos; Pfeiffer, Michael; Plana, Luis A; Furber, Steve B; Benosman, Ryad B

2014-01-01

Many of the precise biological mechanisms of synaptic plasticity remain elusive, but simulations of neural networks have greatly enhanced our understanding of how specific global functions arise from the massively parallel computation of neurons and local Hebbian or spike-timing dependent plasticity rules. For simulating large portions of neural tissue, this has created an increasingly strong need for large scale simulations of plastic neural networks on special purpose hardware platforms, because synaptic transmissions and updates are badly matched to computing style supported by current architectures. Because of the great diversity of biological plasticity phenomena and the corresponding diversity of models, there is a great need for testing various hypotheses about plasticity before committing to one hardware implementation. Here we present a novel framework for investigating different plasticity approaches on the SpiNNaker distributed digital neural simulation platform. The key innovation of the proposed architecture is to exploit the reconfigurability of the ARM processors inside SpiNNaker, dedicating a subset of them exclusively to process synaptic plasticity updates, while the rest perform the usual neural and synaptic simulations. We demonstrate the flexibility of the proposed approach by showing the implementation of a variety of spike- and rate-based learning rules, including standard Spike-Timing dependent plasticity (STDP), voltage-dependent STDP, and the rate-based BCM rule. We analyze their performance and validate them by running classical learning experiments in real time on a 4-chip SpiNNaker board. The result is an efficient, modular, flexible and scalable framework, which provides a valuable tool for the fast and easy exploration of learning models of very different kinds on the parallel and reconfigurable SpiNNaker system.

Cross-Approximate Entropy parallel computation on GPUs for biomedical signal analysis. Application to MEG recordings.

PubMed

Martínez-Zarzuela, Mario; Gómez, Carlos; Díaz-Pernas, Francisco Javier; Fernández, Alberto; Hornero, Roberto

2013-10-01

Cross-Approximate Entropy (Cross-ApEn) is a useful measure to quantify the statistical dissimilarity of two time series. In spite of the advantage of Cross-ApEn over its one-dimensional counterpart (Approximate Entropy), only a few studies have applied it to biomedical signals, mainly due to its high computational cost. In this paper, we propose a fast GPU-based implementation of the Cross-ApEn that makes feasible its use over a large amount of multidimensional data. The scheme followed is fully scalable, thus maximizes the use of the GPU despite of the number of neural signals being processed. The approach consists in processing many trials or epochs simultaneously, with independence of its origin. In the case of MEG data, these trials can proceed from different input channels or subjects. The proposed implementation achieves an average speedup greater than 250× against a CPU parallel version running on a processor containing six cores. A dataset of 30 subjects containing 148 MEG channels (49 epochs of 1024 samples per channel) can be analyzed using our development in about 30min. The same processing takes 5 days on six cores and 15 days when running on a single core. The speedup is much larger if compared to a basic sequential Matlab(®) implementation, that would need 58 days per subject. To our knowledge, this is the first contribution of Cross-ApEn measure computation using GPUs. This study demonstrates that this hardware is, to the day, the best option for the signal processing of biomedical data with Cross-ApEn. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

A framework for plasticity implementation on the SpiNNaker neural architecture

PubMed Central

Galluppi, Francesco; Lagorce, Xavier; Stromatias, Evangelos; Pfeiffer, Michael; Plana, Luis A.; Furber, Steve B.; Benosman, Ryad B.

2015-01-01

Many of the precise biological mechanisms of synaptic plasticity remain elusive, but simulations of neural networks have greatly enhanced our understanding of how specific global functions arise from the massively parallel computation of neurons and local Hebbian or spike-timing dependent plasticity rules. For simulating large portions of neural tissue, this has created an increasingly strong need for large scale simulations of plastic neural networks on special purpose hardware platforms, because synaptic transmissions and updates are badly matched to computing style supported by current architectures. Because of the great diversity of biological plasticity phenomena and the corresponding diversity of models, there is a great need for testing various hypotheses about plasticity before committing to one hardware implementation. Here we present a novel framework for investigating different plasticity approaches on the SpiNNaker distributed digital neural simulation platform. The key innovation of the proposed architecture is to exploit the reconfigurability of the ARM processors inside SpiNNaker, dedicating a subset of them exclusively to process synaptic plasticity updates, while the rest perform the usual neural and synaptic simulations. We demonstrate the flexibility of the proposed approach by showing the implementation of a variety of spike- and rate-based learning rules, including standard Spike-Timing dependent plasticity (STDP), voltage-dependent STDP, and the rate-based BCM rule. We analyze their performance and validate them by running classical learning experiments in real time on a 4-chip SpiNNaker board. The result is an efficient, modular, flexible and scalable framework, which provides a valuable tool for the fast and easy exploration of learning models of very different kinds on the parallel and reconfigurable SpiNNaker system. PMID:25653580

High-performance hardware implementation of a parallel database search engine for real-time peptide mass fingerprinting

PubMed Central

Bogdán, István A.; Rivers, Jenny; Beynon, Robert J.; Coca, Daniel

2008-01-01

Motivation: Peptide mass fingerprinting (PMF) is a method for protein identification in which a protein is fragmented by a defined cleavage protocol (usually proteolysis with trypsin), and the masses of these products constitute a ‘fingerprint’ that can be searched against theoretical fingerprints of all known proteins. In the first stage of PMF, the raw mass spectrometric data are processed to generate a peptide mass list. In the second stage this protein fingerprint is used to search a database of known proteins for the best protein match. Although current software solutions can typically deliver a match in a relatively short time, a system that can find a match in real time could change the way in which PMF is deployed and presented. In a paper published earlier we presented a hardware design of a raw mass spectra processor that, when implemented in Field Programmable Gate Array (FPGA) hardware, achieves almost 170-fold speed gain relative to a conventional software implementation running on a dual processor server. In this article we present a complementary hardware realization of a parallel database search engine that, when running on a Xilinx Virtex 2 FPGA at 100 MHz, delivers 1800-fold speed-up compared with an equivalent C software routine, running on a 3.06 GHz Xeon workstation. The inherent scalability of the design means that processing speed can be multiplied by deploying the design on multiple FPGAs. The database search processor and the mass spectra processor, running on a reconfigurable computing platform, provide a complete real-time PMF protein identification solution. Contact: d.coca@sheffield.ac.uk PMID:18453553

The scalable implementation of quantum walks using classical light

NASA Astrophysics Data System (ADS)

Goyal, Sandeep K.; Roux, F. S.; Forbes, Andrew; Konrad, Thomas

2014-02-01

A quantum walk is the quantum analog of the classical random walks. Despite their simple structure they form a universal platform to implement any algorithm of quantum computation. However, it is very hard to realize quantum walks with a sufficient number of iterations in quantum systems due to their sensitivity to environmental influences and subsequent loss of coherence. Here we present a scalable implementation scheme for one-dimensional quantum walks for arbitrary number of steps using the orbital angular momentum modes of classical light beams. Furthermore, we show that using the same setup with a minor adjustment we can also realize electric quantum walks.

Novel high-fidelity realistic explosion damage simulation for urban environments

NASA Astrophysics Data System (ADS)

Liu, Xiaoqing; Yadegar, Jacob; Zhu, Youding; Raju, Chaitanya; Bhagavathula, Jaya

2010-04-01

Realistic building damage simulation has a significant impact in modern modeling and simulation systems especially in diverse panoply of military and civil applications where these simulation systems are widely used for personnel training, critical mission planning, disaster management, etc. Realistic building damage simulation should incorporate accurate physics-based explosion models, rubble generation, rubble flyout, and interactions between flying rubble and their surrounding entities. However, none of the existing building damage simulation systems sufficiently faithfully realize the criteria of realism required for effective military applications. In this paper, we present a novel physics-based high-fidelity and runtime efficient explosion simulation system to realistically simulate destruction to buildings. In the proposed system, a family of novel blast models is applied to accurately and realistically simulate explosions based on static and/or dynamic detonation conditions. The system also takes account of rubble pile formation and applies a generic and scalable multi-component based object representation to describe scene entities and highly scalable agent-subsumption architecture and scheduler to schedule clusters of sequential and parallel events. The proposed system utilizes a highly efficient and scalable tetrahedral decomposition approach to realistically simulate rubble formation. Experimental results demonstrate that the proposed system has the capability to realistically simulate rubble generation, rubble flyout and their primary and secondary impacts on surrounding objects including buildings, constructions, vehicles and pedestrians in clusters of sequential and parallel damage events.

A comparative study of serial and parallel aeroelastic computations of wings

NASA Technical Reports Server (NTRS)

Byun, Chansup; Guruswamy, Guru P.

1994-01-01

A procedure for computing the aeroelasticity of wings on parallel multiple-instruction, multiple-data (MIMD) computers is presented. In this procedure, fluids are modeled using Euler equations, and structures are modeled using modal or finite element equations. The procedure is designed in such a way that each discipline can be developed and maintained independently by using a domain decomposition approach. In the present parallel procedure, each computational domain is scalable. A parallel integration scheme is used to compute aeroelastic responses by solving fluid and structural equations concurrently. The computational efficiency issues of parallel integration of both fluid and structural equations are investigated in detail. This approach, which reduces the total computational time by a factor of almost 2, is demonstrated for a typical aeroelastic wing by using various numbers of processors on the Intel iPSC/860.

Runtime support for parallelizing data mining algorithms

NASA Astrophysics Data System (ADS)

Jin, Ruoming; Agrawal, Gagan

2002-03-01

With recent technological advances, shared memory parallel machines have become more scalable, and offer large main memories and high bus bandwidths. They are emerging as good platforms for data warehousing and data mining. In this paper, we focus on shared memory parallelization of data mining algorithms. We have developed a series of techniques for parallelization of data mining algorithms, including full replication, full locking, fixed locking, optimized full locking, and cache-sensitive locking. Unlike previous work on shared memory parallelization of specific data mining algorithms, all of our techniques apply to a large number of common data mining algorithms. In addition, we propose a reduction-object based interface for specifying a data mining algorithm. We show how our runtime system can apply any of the technique we have developed starting from a common specification of the algorithm.

Establishing a Novel Modeling Tool: A Python-Based Interface for a Neuromorphic Hardware System

PubMed Central

Brüderle, Daniel; Müller, Eric; Davison, Andrew; Muller, Eilif; Schemmel, Johannes; Meier, Karlheinz

2008-01-01

Neuromorphic hardware systems provide new possibilities for the neuroscience modeling community. Due to the intrinsic parallelism of the micro-electronic emulation of neural computation, such models are highly scalable without a loss of speed. However, the communities of software simulator users and neuromorphic engineering in neuroscience are rather disjoint. We present a software concept that provides the possibility to establish such hardware devices as valuable modeling tools. It is based on the integration of the hardware interface into a simulator-independent language which allows for unified experiment descriptions that can be run on various simulation platforms without modification, implying experiment portability and a huge simplification of the quantitative comparison of hardware and simulator results. We introduce an accelerated neuromorphic hardware device and describe the implementation of the proposed concept for this system. An example setup and results acquired by utilizing both the hardware system and a software simulator are demonstrated. PMID:19562085

Establishing a novel modeling tool: a python-based interface for a neuromorphic hardware system.

PubMed

Brüderle, Daniel; Müller, Eric; Davison, Andrew; Muller, Eilif; Schemmel, Johannes; Meier, Karlheinz

2009-01-01

Neuromorphic hardware systems provide new possibilities for the neuroscience modeling community. Due to the intrinsic parallelism of the micro-electronic emulation of neural computation, such models are highly scalable without a loss of speed. However, the communities of software simulator users and neuromorphic engineering in neuroscience are rather disjoint. We present a software concept that provides the possibility to establish such hardware devices as valuable modeling tools. It is based on the integration of the hardware interface into a simulator-independent language which allows for unified experiment descriptions that can be run on various simulation platforms without modification, implying experiment portability and a huge simplification of the quantitative comparison of hardware and simulator results. We introduce an accelerated neuromorphic hardware device and describe the implementation of the proposed concept for this system. An example setup and results acquired by utilizing both the hardware system and a software simulator are demonstrated.